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Facebook will drop its facial recognition system – but here’s why we should be sceptical

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Facebook will drop its facial recognition system – but here’s why we should be sceptical

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Main idea:
Facebook has announced that it will stop using its facial recognition system – the artificial intelligence software which recognises people in photos and videos and generates suggestions about who to “tag” in them.

Arguments/facts:

A brief history of facial recognition:
Facial recognition was actually introduced in the 1960s when it was used to classify photos of people. But it was the 2010s that really kickstarted the modern era of facial recognition. It was when computers were finally powerful enough to train networks to find facial recognition a standard feature. Soon after, facial recognition was being used for FaceBook tagging and the identification of participants in protests...
There have been concerns about unsolicited facial recognition in public spaces. This is what Brenda Leong has to say about it “You just walk through a set of cameras and all those things happen very seamlessly, sports arenas, event venues, amusement parks, all those places either are using or would have ideas of ways to use it similarly.”

Dialogue:

Summary:
Though facial recognition might seem like quite a modern feature in our day to day lives, it has been around for more than 60 years.

APA:
Stavros Shiaeles, (2021, November 10). Facebook will drop its facial recognition system – but here’s why we should be sceptical
Retrieved from:

https://theconversation.com/facebook-will-drop-its-facial-recognition-system-but-heres-why-we-should-be-sceptical-171186

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Facebook has announced that it will stop using its facial recognition system - the artificial intelligence software which recognises people in photos and videos and generates suggestions about who to "tag" in them.

Facial recognition systems, like Facebook's, identify people by matching faces to digital representations of faces stored on a database. Facebook has more than a billion of these representations on file but now says it will delete them.

This announcement came barely a week after Facebook's parent company rebranded itself from Facebook to Meta. The name change reflects the company's focus on the "metaverse", a vision for the internet which uses technology like virtual reality to integrate real and digital worlds.

The name change probably also had something to do with a desire to detoxify Facebook's image. In recent years, the social media giant has been embroiled in a number of controversies - perhaps most notably the Cambridge Analytica scandal.

This saw an app use Facebook's platform to harvest personal data belonging to millions of Facebook users, which was then passed to Cambridge Analytica, a now defunct British consulting firm. In 2018, the UK's data protection watchdog, the Information Commissioner's Office, fined Facebook £500,000 for its role in the scandal.

More recently we've heard former Facebook product manager Frances Haugen claim that the platform harms children, stokes division and undermines democracy in pursuit of fast growth and " astronomical profits ".

We might wonder whether the facial recognition move, too, is an attempt to present a new, responsible image focused on respecting and protecting users' privacy.

Read more: Class action against Facebook over facial recognition could pave the way for further lawsuits

Our data is like gold

Facebook is free to join and use so it relies on another valuable product to cover its expenses - people's data.

As part of my team's research, we got permission from a group of Facebook users, and had crawlers (bots that systematically browse the internet) collect their posts and pictures - or posts and pictures which featured them. Using machine learning algorithms on this data, we were able to profile their habits and predict with high accuracy things like where they would be the next day.

In a related study, we looked at Facebook wall posts and, again using machine learning, we were able to build a psychological profile of users based on their posts. That is, we could ascertain when they were sad, happy, and so on.

If I can gather data from Facebook using a relatively simple program and come up with accurate conclusions, imagine what Facebook can do with its vast amount of data - including from our face templates - and artificial intelligence.

Amid privacy concerns about the technology, in 2019, Facebook made the facial recognition feature opt-in. Last year, Facebook agreed to pay a US$650 million settlement (roughly £480 million) after a lawsuit claimed its facial recognition system violated Illinois' Biometric Information Privacy Act.

While many might construe Meta's announcement as a positive development, I see it as a convenient distraction from, or perhaps a countermeasure to, the whistleblower testimonies presenting a company that puts profits before user safety.

It's also worth pointing out that Facebook is struggling to retain young users, so they're probably looking for ways to attract this important group to the platform.

Read more: Facebook, the metaverse and the monetisation of higher education

It's not gone completely

Meta's announcement specified facial recognition technology would be limited to "a narrow set of use cases" moving forward. This could include verifying a user's identity so they can gain access to a locked account, for example.

As such, Meta is reportedly keeping DeepFace, the algorithm behind its facial recognition technology. Meta spokesperson Jason Grosse said the company hasn't ruled out using facial recognition technology in future products. Notably, Grosse has also reportedly said the commitment to stop facial recognition doesn't apply to its metaverse products.

Grosse told the publication Recode:

We believe this technology has the potential to enable positive use cases in the future that maintain privacy, control, and transparency, and it's an approach we'll continue to explore as we consider how our future computing platforms and devices can best serve people's needs [...] For any potential future applications of technologies like this, we'll continue to be public about intended use, how people can have control over these systems and their personal data, and how we're living up to our responsible innovation framework.

Read more: Metaverse: five things to know - and what it could mean for you

It's important to understand that when a person engages in a virtual reality environment in the metaverse, they will generate a range of biometric data, well beyond facial scans. For example, depending on the system, it may be possible to detect and collect eye movements, body movements, blood pressure, heart rate, and details about the users' environment.

Ultimately, the artificial intelligence accompanying the metaverse will be much more sophisticated and likely bring with it a new set of data privacy issues.

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Facebook will drop its facial recognition system - but here's why we should be sceptical

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Facebook has announced that it will stop using its facial recognition system - the artificial intelligence software which recognises people in photos and videos and generates suggestions about who to "tag" in them. Facial recognition systems, like Facebook's, identify people by matching faces to digital representations of faces stored on a database.

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Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as biometric technology is lower than iris recognition and fingerprint recognition, it is widely adopted due to its contactless process. Facial recognition systems have been deployed in advanced human-computer interaction, video surveillance and automatic indexing of images.

Facial recognition systems are employed throughout the world today by governments and private companies. Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. These claims have led to the ban of facial recognition systems in several cities in the United States. As a result of growing societal concerns, Meta announced that it plans to shut down Facebook facial recognition system, deleting the face scan data of more than one billion users. This change will represent one of the largest shifts in facial recognition usage in the technology's history.

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Facial recognition software at a US airport

Automatic ticket gate with face recognition system in Osaka Metro Morinomiya Station

A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.

Development began on similar systems in the 1960s, beginning as a form of computer application. Since their inception, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics. Because computerized facial recognition involves the measurement of a human's physiological characteristics, facial recognition systems are categorized as biometrics. Although the accuracy of facial recognition systems as a biometric technology is lower than iris recognition, fingerprint image acquisition, palm recognition or voice recognition, it is widely adopted due to its contactless process. Facial recognition systems have been deployed in advanced human-computer interaction, video surveillance, law enforcement, passenger screening, decisions on employment and housing and automatic indexing of images.

Facial recognition systems are employed throughout the world today by governments and private companies. Their effectiveness varies, and some systems have previously been scrapped because of their ineffectiveness. The use of facial recognition systems has also raised controversy, with claims that the systems violate citizens' privacy, commonly make incorrect identifications, encourage gender norms and racial profiling, and do not protect important biometric data. The appearance of synthetic media such as deepfakes has also raised concerns about its security. These claims have led to the ban of facial recognition systems in several cities in the United States. Growing societal concerns led social networking company Meta Platforms to shut down its Facebook facial recognition system in 2021, deleting the face scan data of more than one billion users. The change represented one of the largest shifts in facial recognition usage in the technology's history. IBM also stopped offering facial recognition technology due to similar concerns.

Automated facial recognition was pioneered in the 1960s by Woody Bledsoe, Helen Chan Wolf, and Charles Bisson, whose work focused on teaching computers to recognize human faces. Their early facial recognition project was dubbed "man-machine" because a human first needed to establish the coordinates of facial features in a photograph before they could be used by a computer for recognition. Using a graphics tablet, a human would pinpoint facial features coordinates, such as the pupil centers, the inside and outside corners of eyes, and the widows peak in the hairline. The coordinates were used to calculate 20 individual distances, including the width of the mouth and of the eyes. A human could process about 40 pictures an hour, building a database of these computed distances. A computer would then automatically compare the distances for each photograph, calculate the difference between the distances, and return the closed records as a possible match.

In 1993, the Defense Advanced Research Project Agency (DARPA) and the Army Research Laboratory (ARL) established the face recognition technology program FERET to develop "automatic face recognition capabilities" that could be employed in a productive real life environment "to assist security, intelligence, and law enforcement personnel in the performance of their duties." Face recognition systems that had been trialed in research labs were evaluated and the FERET tests found that while the performance of existing automated facial recognition systems varied, a handful of existing methods could viably be used to recognize faces in still images taken in a controlled environment. The FERET tests spawned three US companies that sold automated facial recognition systems. Vision Corporation and Miros Inc were both founded in 1994, by researchers who used the results of the FERET tests as a selling point. Viisage Technology was established by a identification card defense contractor in 1996 to commercially exploit the rights to the facial recognition algorithm developed by Alex Pentland at MIT.

Following the 1993 FERET face-recognition vendor test, the Department of Motor Vehicles (DMV) offices in West Virginia and New Mexico became the first DMV offices to use automated facial recognition systems to prevent people from obtaining multiple driving licenses using different names. Driver's licenses in the United States were at that point a commonly accepted form of photo identification. DMV offices across the United States were undergoing a technological upgrade and were in the process of establishing databases of digital ID photographs. This enabled DMV offices to deploy the facial recognition systems on the market to search photographs for new driving licenses against the existing DMV database. DMV offices became one of the first major markets for automated facial recognition technology and introduced US citizens to facial recognition as a standard method of identification. The increase of the US prison population in the 1990s prompted U.S. states to established connected and automated identification systems that incorporated digital biometric databases, in some instances this included facial recognition. In 1999, Minnesota incorporated the facial recognition system FaceIT by Visionics into a mug shot booking system that allowed police, judges and court officers to track criminals across the state.

In this shear mapping the red arrow changes direction, but the blue arrow does not and is used as eigenvector.

The Viola-Jones algorithm for face detection uses Haar-like features to locate faces in an image. Here a Haar feature that looks similar to the bridge of the nose is applied onto the face.

Until the 1990s, facial recognition systems were developed primarily by using photographic portraits of human faces. Research on face recognition to reliably locate a face in an image that contains other objects gained traction in the early 1990s with the principal component analysis (PCA). The PCA method of face detection is also known as Eigenface and was developed by Matthew Turk and Alex Pentland. Turk and Pentland combined the conceptual approach of the Karhunen-Loève theorem and factor analysis, to develop a linear model. Eigenfaces are determined based on global and orthogonal features in human faces. A human face is calculated as a weighted combination of a number of Eigenfaces. Because few Eigenfaces were used to encode human faces of a given population, Turk and Pentland's PCA face detection method greatly reduced the amount of data that had to be processed to detect a face. Pentland in 1994 defined Eigenface features, including eigen eyes, eigen mouths and eigen noses, to advance the use of PCA in facial recognition. In 1997, the PCA Eigenface method of face recognition was improved upon using linear discriminant analysis (LDA) to produce Fisherfaces. LDA Fisherfaces became dominantly used in PCA feature based face recognition. While Eigenfaces were also used for face reconstruction. In these approaches no global structure of the face is calculated which links the facial features or parts.

Purely feature based approaches to facial recognition were overtaken in the late 1990s by the Bochum system, which used Gabor filter to record the face features and computed a grid of the face structure to link the features. Christoph von der Malsburg and his research team at the University of Bochum developed Elastic Bunch Graph Matching in the mid-1990s to extract a face out of an image using skin segmentation. By 1997, the face detection method developed by Malsburg outperformed most other facial detection systems on the market. The so-called "Bochum system" of face detection was sold commercially on the market as ZN-Face to operators of airports and other busy locations. The software was "robust enough to make identifications from less-than-perfect face views. It can also often see through such impediments to identification as mustaches, beards, changed hairstyles and glasses-even sunglasses".

Real-time face detection in video footage became possible in 2001 with the Viola-Jones object detection framework for faces. Paul Viola and Michael Jones combined their face detection method with the Haar-like feature approach to object recognition in digital images to launch AdaBoost, the first real-time frontal-view face detector. By 2015, the Viola-Jones algorithm had been implemented using small low power detectors on handheld devices and embedded systems. Therefore, the Viola-Jones algorithm has not only broadened the practical application of face recognition systems but has also been used to support new features in user interfaces and teleconferencing.

Ukraine is using the US-based Clearview AI facial recognition software to identify dead Russian soldiers. Ukraine has conducted 8,600 searches and identified the families of 582 deceased Russian soldiers. The IT volunteer section of the Ukrainian army using the software is subsequently contacting the families of the deceased soldiers to raise awareness of Russian activities in Ukraine. The main goal is to destabilise the Russian government. It can be seen as a form of psychological warfare. About 340 Ukrainian government officials in five government ministries are using the technology. It is used to catch spies that might try to enter Ukraine.

Clearview AI's facial recognition database is only available to government agencies who may only use the technology to assist in the course of law enforcement investigations or in connection with national security.

The software was donated to Ukraine by Clearview AI. Russia is thought to be using it to find anti-war activists. Clearview AI was originally designed for US law enforcement. Using it in war raises new ethical concerns. One London based surveillance expert, Stephen Hare, is concerned it might make the Ukrainians appear inhuman: "Is it actually working? Or is it making [Russians] say: 'Look at these lawless, cruel Ukrainians, doing this to our boys'?"

Automatic face detection with OpenCV

While humans can recognize faces without much effort, facial recognition is a challenging pattern recognition problem in computing. Facial recognition systems attempt to identify a human face, which is three-dimensional and changes in appearance with lighting and facial expression, based on its two-dimensional image. To accomplish this computational task, facial recognition systems perform four steps. First face detection is used to segment the face from the image background. In the second step the segmented face image is aligned to account for face pose, image size and photographic properties, such as illumination and grayscale. The purpose of the alignment process is to enable the accurate localization of facial features in the third step, the facial feature extraction. Features such as eyes, nose and mouth are pinpointed and measured in the image to represent the face. The so established feature vector of the face is then, in the fourth step, matched against a database of faces.

Recognition algorithms can be divided into two main approaches: geometric, which looks at distinguishing features, or photo-metric, which is a statistical approach that distills an image into values and compares the values with templates to eliminate variances. Some classify these algorithms into two broad categories: holistic and feature-based models. The former attempts to recognize the face in its entirety while the feature-based subdivide into components such as according to features and analyze each as well as its spatial location with respect to other features.

Popular recognition algorithms include principal component analysis using eigenfaces, linear discriminant analysis, elastic bunch graph matching using the Fisherface algorithm, the hidden Markov model, the multilinear subspace learning using tensor representation, and the neuronal motivated dynamic link matching.[citation needed ] Modern facial recognition systems make increasing use of machine learning techniques such as deep learning.

Human identification at a distance (HID)

To enable human identification at a distance (HID) low-resolution images of faces are enhanced using face hallucination. In CCTV imagery faces are often very small. But because facial recognition algorithms that identify and plot facial features require high resolution images, resolution enhancement techniques have been developed to enable facial recognition systems to work with imagery that has been captured in environments with a high signal-to-noise ratio. Face hallucination algorithms that are applied to images prior to those images being submitted to the facial recognition system use example-based machine learning with pixel substitution or nearest neighbour distribution indexes that may also incorporate demographic and age related facial characteristics. Use of face hallucination techniques improves the performance of high resolution facial recognition algorithms and may be used to overcome the inherent limitations of super-resolution algorithms. Face hallucination techniques are also used to pre-treat imagery where faces are disguised. Here the disguise, such as sunglasses, is removed and the face hallucination algorithm is applied to the image. Such face hallucination algorithms need to be trained on similar face images with and without disguise. To fill in the area uncovered by removing the disguise, face hallucination algorithms need to correctly map the entire state of the face, which may be not possible due to the momentary facial expression captured in the low resolution image.

3D model of a human face

Three-dimensional face recognition technique uses 3D sensors to capture information about the shape of a face. This information is then used to identify distinctive features on the surface of a face, such as the contour of the eye sockets, nose, and chin. One advantage of 3D face recognition is that it is not affected by changes in lighting like other techniques. It can also identify a face from a range of viewing angles, including a profile view. Three-dimensional data points from a face vastly improve the precision of face recognition. 3D-dimensional face recognition research is enabled by the development of sophisticated sensors that project structured light onto the face. 3D matching technique are sensitive to expressions, therefore researchers at Technion applied tools from metric geometry to treat expressions as isometries. A new method of capturing 3D images of faces uses three tracking cameras that point at different angles; one camera will be pointing at the front of the subject, second one to the side, and third one at an angle. All these cameras will work together so it can track a subject's face in real-time and be able to face detect and recognize.

A pseudocolor image of two people taken in long-wavelength infrared (body-temperature thermal) light

A different form of taking input data for face recognition is by using thermal cameras, by this procedure the cameras will only detect the shape of the head and it will ignore the subject accessories such as glasses, hats, or makeup. Unlike conventional cameras, thermal cameras can capture facial imagery even in low-light and nighttime conditions without using a flash and exposing the position of the camera. However, the databases for face recognition are limited. Efforts to build databases of thermal face images date back to 2004. By 2016, several databases existed, including the IIITD-PSE and the Notre Dame thermal face database. Current thermal face recognition systems are not able to reliably detect a face in a thermal image that has been taken of an outdoor environment.

In 2018, researchers from the U.S. Army Research Laboratory (ARL) developed a technique that would allow them to match facial imagery obtained using a thermal camera with those in databases that were captured using a conventional camera. Known as a cross-spectrum synthesis method due to how it bridges facial recognition from two different imaging modalities, this method synthesize a single image by analyzing multiple facial regions and details. It consists of a non-linear regression model that maps a specific thermal image into a corresponding visible facial image and an optimization issue that projects the latent projection back into the image space. ARL scientists have noted that the approach works by combining global information (i.e. features across the entire face) with local information (i.e. features regarding the eyes, nose, and mouth). According to performance tests conducted at ARL, the multi-region cross-spectrum synthesis model demonstrated a performance improvement of about 30% over baseline methods and about 5% over state-of-the-art methods.

Founded in 2013, Looksery went on to raise money for its face modification app on Kickstarter. After successful crowdfunding, Looksery launched in October 2014. The application allows video chat with others through a special filter for faces that modifies the look of users. Image augmenting applications already on the market, such as Facetune and Perfect365, were limited to static images, whereas Looksery allowed augmented reality to live videos. In late 2015 SnapChat purchased Looksery, which would then become its landmark lenses function. Snapchat filter applications use face detection technology and on the basis of the facial features identified in an image a 3D mesh mask is layered over the face. A variety of technologies attempt to fool facial recognition software by the use of anti-facial recognition masks.

DeepFace is a deep learning facial recognition system created by a research group at Facebook. It identifies human faces in digital images. It employs a nine-layer neural net with over 120 million connection weights, and was trained on four million images uploaded by Facebook users. The system is said to be 97% accurate, compared to 85% for the FBI's Next Generation Identification system.

The emerging use of facial recognition is in the use of ID verification services. Many companies and others are working in the market now to provide these services to banks, ICOs, and other e-businesses. Face recognition has been leveraged as a form of biometric authentication for various computing platforms and devices; Android 4.0 "Ice Cream Sandwich" added facial recognition using a smartphone's front camera as a means of unlocking devices, while Microsoft introduced face recognition login to its Xbox 360 video game console through its Kinect accessory, as well as Windows 10 via its "Windows Hello" platform (which requires an infrared-illuminated camera). In 2017, Apple's iPhone X smartphone introduced facial recognition to the product line with its " Face ID" platform, which uses an infrared illumination system.

Apple introduced Face ID on the flagship iPhone X as a biometric authentication successor to the Touch ID, a fingerprint based system. Face ID has a facial recognition sensor that consists of two parts: a "Romeo" module that projects more than 30,000 infrared dots onto the user's face, and a "Juliet" module that reads the pattern. The pattern is sent to a local "Secure Enclave" in the device's central processing unit (CPU) to confirm a match with the phone owner's face.

The facial pattern is not accessible by Apple. The system will not work with eyes closed, in an effort to prevent unauthorized access. The technology learns from changes in a user's appearance, and therefore works with hats, scarves, glasses, and many sunglasses, beard and makeup. It also works in the dark. This is done by using a "Flood Illuminator", which is a dedicated infrared flash that throws out invisible infrared light onto the user's face to properly read the 30,000 facial points.

In March, 2022 according to a publication by Forbes, FDNA, an AI development company claimed that in the space of 10 years, they have worked with geneticists to develop a database of about 5,000 diseases and 1500 of them can be detected with facial recognition algorithms.

In an interview, the National Health Authority chief Dr. R.S. Sharma said that facial recognition technology would be used in conjunction with Aadhaar to authenticate the identity of people seeking vaccines. Ten human rights and digital rights organizations and more than 150 individuals signed a statement by the Internet Freedom Foundation that raised alarm against the deployment of facial recognition technology in the central government's vaccination drive process. Implementation of an error-prone system without adequate legislation containing mandatory safeguards, would deprive citizens of essential services and linking this untested technology to the vaccination roll-out in India will only exclude persons from the vaccine delivery system.

In July, 2021, a press release by the Government of Meghalaya stated that facial recognition technology (FRT) would be used to verify the identity of pensioners to issue a Digital Life Certificate using "Pensioner's Life Certification Verification" mobile application. The notice, according to the press release, purports to offer pensioners "a secure, easy and hassle-free interface for verifying their liveness to the Pension Disbursing Authorities from the comfort of their homes using smart phones". Mr. Jade Jeremiah Lyngdoh, a law student, sent a legal notice to the relevant authorities highlighting that "The application has been rolled out without any anchoring legislation which governs the processing of personal data and thus, lacks lawfulness and the Government is not empowered to process data."

The Australian Border Force and New Zealand Customs Service have set up an automated border processing system called SmartGate that uses face recognition, which compares the face of the traveller with the data in the e-passport microchip. All Canadian international airports use facial recognition as part of the Primary Inspection Kiosk program that compares a traveler face to their photo stored on the ePassport. This program first came to Vancouver International Airport in early 2017 and was rolled up to all remaining international airports in 2018-2019.

Police forces in the United Kingdom have been trialing live facial recognition technology at public events since 2015. In May 2017, a man was arrested using an automatic facial recognition (AFR) system mounted on a van operated by the South Wales Police. Ars Technica reported that "this appears to be the first time [AFR] has led to an arrest". However, a 2018 report by Big Brother Watch found that these systems were up to 98% inaccurate. The report also revealed that two UK police forces, South Wales Police and the Metropolitan Police, were using live facial recognition at public events and in public spaces. In September 2019, South Wales Police use of facial recognition was ruled lawful. Live facial recognition has been trialled since 2016 in the streets of London and will be used on a regular basis from Metropolitan Police from beginning of 2020. In August 2020 the Court of Appeal ruled that the way the facial recognition system had been used by the South Wales Police in 2017 and 2018 violated human rights.

The U.S. Department of State operates one of the largest face recognition systems in the world with a database of 117 million American adults, with photos typically drawn from driver's license photos. Although it is still far from completion, it is being put to use in certain cities to give clues as to who was in the photo. The FBI uses the photos as an investigative tool, not for positive identification. As of 2016, facial recognition was being used to identify people in photos taken by police in San Diego and Los Angeles (not on real-time video, and only against booking photos) and use was planned in West Virginia and Dallas.

The FBI has also instituted its Next Generation Identification program to include face recognition, as well as more traditional biometrics like fingerprints and iris scans, which can pull from both criminal and civil databases. The federal General Accountability Office criticized the FBI for not addressing various concerns related to privacy and accuracy.

Starting in 2018, U.S. Customs and Border Protection deployed "biometric face scanners" at U.S. airports. Passengers taking outbound international flights can complete the check-in, security and the boarding process after getting facial images captured and verified by matching their ID photos stored on CBP's database. Images captured for travelers with U.S. citizenship will be deleted within up to 12-hours. Transportation Security Administration (TSA) had expressed its intention to adopt a similar program for domestic air travel during the security check process in the future. The American Civil Liberties Union is one of the organizations against the program, concerning that the program will be used for surveillance purposes.

In December 2022, 16 major domestic airports in the US started testing facial-recognition tech where kiosks with cameras are checking the photos on travelers' IDs to make sure that passengers are not impostors.

Boarding gates with facial recognition technology at Beijing West railway station

As of late 2017, China has deployed facial recognition and artificial intelligence technology in Xinjiang. Reporters visiting the region found surveillance cameras installed every hundred meters or so in several cities, as well as facial recognition checkpoints at areas like gas stations, shopping centers, and mosque entrances. In May 2019, Human Rights Watch reported finding Face++ code in the Integrated Joint Operations Platform (IJOP), a police surveillance app used to collect data on, and track the Uighur community in Xinjiang. Human Rights Watch released a correction to its report in June 2019 stating that the Chinese company Megvii did not appear to have collaborated on IJOP, and that the Face++ code in the app was inoperable. In February 2020, following the Coronavirus outbreak, Megvii applied for a bank loan to optimize the body temperature screening system it had launched to help identify people with symptoms of a Coronavirus infection in crowds. In the loan application Megvii stated that it needed to improve the accuracy of identifying masked individuals.

Many public places in China are implemented with facial recognition equipment, including railway stations, airports, tourist attractions, expos, and office buildings. In October 2019, a professor at Zhejiang Sci-Tech University sued the Hangzhou Safari Park for abusing private biometric information of customers. The safari park uses facial recognition technology to verify the identities of its Year Card holders. An estimated 300 tourist sites in China have installed facial recognition systems and use them to admit visitors. This case is reported to be the first on the use of facial recognition systems in China. In August 2020, Radio Free Asia reported that in 2019 Geng Guanjun, a citizen of Taiyuan City who had used the WeChat app by Tencent to forward a video to a friend in the United States was subsequently convicted on the charge of the crime "picking quarrels and provoking troubles". The Court documents showed that the Chinese police used a facial recognition system to identify Geng Guanjun as an "overseas democracy activist" and that China's network management and propaganda departments directly monitor WeChat users.

In 2019, Protestors in Hong Kong destroyed smart lampposts amid concerns they could contain cameras and facial recognition system used for surveillance by Chinese authorities. Human rights groups have criticized the Chinese government for using artificial intelligence facial recognition technology in its suppression against Uyghurs, Christians and Falun Gong practitioners.

Even though facial recognition technology (FRT) is not fully accurate, it is being increasingly deployed for identification purposes by the police in India. FRT systems generate a probability match score, or a confidence score between the suspect who is to be identified and the database of identified criminals that is available with the police. The National Automated Facial Recognition System (AFRS) is already being developed by the National Crime Records Bureau (NCRB), a body constituted under the Ministry of Home Affairs. The project seeks to develop and deploy a national database of photographs which would comport with a facial recognition technology system by the central and state security agencies. The Internet Freedom Foundation has flagged concerns regarding the project. The NGO has highlighted that the accuracy of FRT systems are "routinely exaggerated and the real numbers leave much to be desired. The implementation of such faulty FRT systems would lead to high rates of false positives and false negatives in this recognition process."

Under the Supreme Court of India's decision in Justice K.S. Puttaswamy vs Union of India (22017 10 SCC 1), any justifiable intrusion by the State into people's right to privacy, which is protected as a fundamental right under Article 21 of the Constitution, must confirm to certain thresholds, namely: legality, necessity, proportionality and procedural safeguards. As per the Internet Freedom Foundation, the National Automated Facial Recognition System (AFRS) proposal fails to meet any of these thresholds, citing "absence of legality," "manifest arbitrariness," and "absence of safeguards and accountability."

While the national level AFRS project is still in the works, police departments in various states in India are already deploying facial recognition technology systems, such as: TSCOP + CCTNS in Telangana, Punjab Artificial Intelligence System (PAIS) in Punjab, Trinetra in Uttar Pradesh, Police Artificial Intelligence System in Uttarakhand, AFRS in Delhi, Automated Multimodal Biometric Identification System (AMBIS) in Maharashtra, FaceTagr in Tamil Nadu. The Crime and Criminal Tracking Network and Systems (CCTNS), which is a Mission Mode Project under the National e-Governance Plan (NeGP), is viewed as a system which would connect police stations across India, and help them "talk" to each other. The project's objective is to digitize all FIR-related information, including FIRs registered, as well as cases investigated, charge sheets filed, and suspects and wanted persons in all police stations. This shall constitute a national database of crime and criminals in India. CCTNS is being implemented without a data protection law in place. CCTNS is proposed to be integrated with the AFRS, a repository of all crime and criminal related facial data which can be deployed to purportedly identify or verify a person from a variety of inputs ranging from images to videos. This has raised privacy concerns from civil society organizations and privacy experts. Both the projects have been censured as instruments of " mass surveillance" at the hands of the state. In Rajasthan, 'RajCop,' a police app has been recently integrated with a facial recognition module which can match the face of a suspect against a database of known persons in real-time. Rajasthan police is in currently working to widen the ambit of this module by making it mandatory to upload photographs of all arrested persons in CCTNS database, which will "help develop a rich database of known offenders."

Helmets fixed with camera have been designed and being used by Rajasthan police in law and order situations to capture police action and activities of "the miscreants, which can later serve as evidence during the investigation of such cases." PAIS (Punjab Artificial Intelligence System), App employs deep learning, machine learning, and face recognition for the identification of criminals to assist police personnel. The state of Telangana has installed 8 lakh CCTV cameras, with its capital city Hyderabad slowly turning into a surveillance capital.

A false positive happens when facial recognition technology misidentifies a person to be someone they are not, that is, it yields an incorrect positive result. They often results in discrimination and strengthening of existing biases. For example, in 2018, Delhi Police reported that its FRT system had an accuracy rate of 2%, which sank to 1% in 2019. The FRT system even failed to distinguish accurately between different sexes.

In a reply dated November 25, 2020 to a Right to Information request filed by the Internet Freedom Foundation seeking information about the facial recognition system being used by the Delhi Police (with reference number DEPOL/R/E/20/07128), the Office of the Deputy Commissioner of Police cum Public Information Officer: Crime stated that they cannot provide the information under section 8(d) of the Right to Information Act, 2005. A Right to Information (RTI) request dated July 30, 2020 was filed with the Office of the Commissioner, Kolkata Police, seeking information about the facial recognition technology that the department was using. The information sought was denied stating that the department was exempted from disclosure under section 24(4) of the RTI Act.

In the 2000 Mexican presidential election, the Mexican government employed face recognition software to prevent voter fraud. Some individuals had been registering to vote under several different names, in an attempt to place multiple votes. By comparing new face images to those already in the voter database, authorities were able to reduce duplicate registrations.

In Colombia public transport busses are fitted with a facial recognition system by FaceFirst Inc to identify passengers that are sought by the National Police of Colombia. FaceFirst Inc also built the facial recognition system for Tocumen International Airport in Panama. The face recognition system is deployed to identify individuals among the travelers that are sought by the Panamanian National Police or Interpol. Tocumen International Airport operates an airport-wide surveillance system using hundreds of live face recognition cameras to identify wanted individuals passing through the airport. The face recognition system was initially installed as part of a US$11 million contract and included a computer cluster of sixty computers, a fiber-optic cable network for the airport buildings, as well as the installation of 150 surveillance cameras in the airport terminal and at about 30 airport gates.

At the 2014 FIFA World Cup in Brazil the Federal Police of Brazil used face recognition goggles. Face recognition systems "made in China" were also deployed at the 2016 Summer Olympics in Rio de Janeiro. Nuctech Company provided 145 insepction terminals for Maracanã Stadium and 55 terminals for the Deodoro Olympic Park.

Police forces in at least 21 countries of the European Union use, or plan to use, facial recognition systems, either for administrative or criminal purposes.

Italian police acquired a face recognition system in 2017, Sistema Automatico Riconoscimento Immagini (SARI). In November 2020, the Interior ministry announced plans to use it in real-time to identify people suspected of seeking asylum.

The Netherlands

In South Africa, in 2016, the city of Johannesburg announced it was rolling out smart CCTV cameras complete with automatic number plate recognition and facial recognition.

The US firm 3VR, now Identiv, is an example of a vendor which began offering facial recognition systems and services to retailers as early as 2007. In 2012, the company advertised benefits such as "dwell and queue line analytics to decrease customer wait times", "facial surveillance analytic[s] to facilitate personalized customer greetings by employees" and the ability to "[c]reate loyalty programs by combining Point of sale (POS) data with facial recognition".

In July 2020, the Reuters news agency reported that during the 2010s the pharmacy chain Rite Aid had deployed facial recognition video surveillance systems and components from FaceFirst, DeepCam LLC, and other vendors at some retail locations in the United States. Cathy Langley, Rite Aid's vice president of asset protection, used the phrase "feature matching" to refer to the systems and said that usage of the systems resulted in less violence and organized crime in the company's stores, while former vice president of asset protection Bob Oberosler emphasized improved safety for staff and a reduced need for the involvement of law enforcement organizations. In a 2020 statement to Reuters in response to the reporting, Rite Aid said that it had ceased using the facial recognition software and switched off the cameras.

According to director Read Hayes of the National Retail Federation Loss Prevention Research Council, Rite Aid's surveillance program was either the largest or one of the largest programs in retail. The Home Depot, Menards, Walmart, and 7-Eleven are among other US retailers also engaged in large-scale pilot programs or deployments of facial recognition technology.

Of the Rite Aid stores examined by Reuters in 2020, those in communities where people of color made up the largest racial or ethnic group were three times as likely to have the technology installed, raising concerns related to the substantial history of racial segregation and racial profiling in the United States. Rite Aid said that the selection of locations was "data-driven", based on the theft histories of individual stores, local and national crime data, and site infrastructure.

At the American football championship game Super Bowl XXXV in January 2001, police in Tampa Bay, Florida used Viisage face recognition software to search for potential criminals and terrorists in attendance at the event. 19 people with minor criminal records were potentially identified.

Face recognition systems have also been used by photo management software to identify the subjects of photographs, enabling features such as searching images by person, as well as suggesting photos to be shared with a specific contact if their presence were detected in a photo. By 2008 facial recognition systems were typically used as access control in security systems.

The United States' popular music and country music celebrity Taylor Swift surreptitiously employed facial recognition technology at a concert in 2018. The camera was embedded in a kiosk near a ticket booth and scanned concert-goers as they entered the facility for known stalkers.

On August 18, 2019, reported that the UAE-owned Manchester City hired a Texas-based firm, Blink Identity, to deploy facial recognition systems in a driver program. The club has planned a single super-fast lane for the supporters at the Etihad stadium. However, civil rights groups cautioned the club against the introduction of this technology, saying that it would risk "normalising a mass surveillance tool". The policy and campaigns officer at Liberty, Hannah Couchman said that Man City's move is alarming, since the fans will be obliged to share deeply sensitive personal information with a private company, where they could be tracked and monitored in their everyday lives.

In 2019, casinos in Australia and New Zealand rolled out facial recognition to prevent theft, and a representative of Sydney's Star Casino said they would also provide 'customer service' like welcoming a patron back to a bar.

In August 2020, amid the COVID-19 pandemic in the United States, American football stadiums of New York and Los Angeles announced the installation of facial recognition for upcoming matches. The purpose is to make the entry process as touchless as possible. Disney's Magic Kingdom, near Orlando, Florida, likewise announced a test of facial recognition technology to create a touchless experience during the pandemic; the test was originally slated to take place between March 23 and April 23, 2021, but the limited timeframe had been removed as of late April 2021.

Media companies have begun using face recognition technology to streamline their tracking, organizing, and archiving pictures and videos.

One key advantage of a facial recognition system that it is able to perform mass identification as it does not require the cooperation of the test subject to work. Properly designed systems installed in airports, multiplexes, and other public places can identify individuals among the crowd, without passers-by even being aware of the system. However, as compared to other biometric techniques, face recognition may not be most reliable and efficient. Quality measures are very important in facial recognition systems as large degrees of variations are possible in face images. Factors such as illumination, expression, pose and noise during face capture can affect the performance of facial recognition systems. Among all biometric systems, facial recognition has the highest false acceptance and rejection rates, thus questions have been raised on the effectiveness of or bias of face recognition software in cases of railway and airport security, law enforcement and housing and employment decisions.

There is also inconstancy in the datasets used by researchers. Researchers may use anywhere from several subjects to scores of subjects and a few hundred images to thousands of images. Data sets may be diverse and inclusive or mainly contain images of white males. It is important for researchers to make available the datasets they used to each other, or have at least a standard or representative dataset.

Although high degrees of accuracy have been claimed for some facial recognition systems, these outcomes are not universal. The consistently worst accuracy rate is for those who are 18 to 30 years old, Black and female.

Facial recognition systems have been criticized for upholding and judging based on assumptions about people of colour and also on a binary gender assumption. When classifying the faces of cisgender individuals into male or female, these systems are often fairly accurate, however were typically confused or unable to determine the gender identity of transgender and non-binary people. Gender norms are being upheld by these systems, so much so that even when shown a photo of a cisgender male with long hair, algorithms were split between following the gender norm of males having short hair, and the masculine facial features and became confused. This accidental misgendering of people can be very harmful for those who do not identify with their sex assigned at birth, by disregarding and invalidating their gender identity. This is also harmful for people who do not ascribe to traditional gender norms, because it invalidates their gender expression, regardless of their gender identity.

An experiment in 2002 by the local police department in Tampa, Florida, had similarly disappointing results. A system at Boston's Logan Airport was shut down in 2003 after failing to make any matches during a two-year test period.

In 2014, Facebook stated that in a standardized two-option facial recognition test, its online system scored 97.25% accuracy, compared to the human benchmark of 97.5%.

Systems are often advertised as having accuracy near 100%; this is misleading as the outcomes are not universal The studies often use samples that are smaller and less diverse than would be necessary for large scale applications. Because facial recognition is not completely accurate, it creates a list of potential matches. A human operator must then look through these potential matches and studies show the operators pick the correct match out of the list only about half the time. This causes the issue of targeting the wrong suspect.

Civil rights organizations and privacy campaigners such as the Electronic Frontier Foundation, Big Brother Watch and the ACLU express concern that privacy is being compromised by the use of surveillance technologies. Face recognition can be used not just to identify an individual, but also to unearth other personal data associated with an individual - such as other photos featuring the individual, blog posts, social media profiles, Internet behavior, and travel patterns. Concerns have been raised over who would have access to the knowledge of one's whereabouts and people with them at any given time. Moreover, individuals have limited ability to avoid or thwart face recognition tracking unless they hide their faces. This fundamentally changes the dynamic of day-to-day privacy by enabling any marketer, government agency, or random stranger to secretly collect the identities and associated personal information of any individual captured by the face recognition system. Consumers may not understand or be aware of what their data is being used for, which denies them the ability to consent to how their personal information gets shared.

In July 2015, the United States Government Accountability Office conducted a Report to the Ranking Member, Subcommittee on Privacy, Technology and the Law, Committee on the Judiciary, U.S. Senate. The report discussed facial recognition technology's commercial uses, privacy issues, and the applicable federal law. It states that previously, issues concerning facial recognition technology were discussed and represent the need for updating the privacy laws of the United States so that federal law continually matches the impact of advanced technologies. The report noted that some industry, government, and private organizations were in the process of developing, or have developed, "voluntary privacy guidelines". These guidelines varied between the stakeholders, but their overall aim was to gain consent and inform citizens of the intended use of facial recognition technology. According to the report the voluntary privacy guidelines helped to counteract the privacy concerns that arise when citizens are unaware of how their personal data gets put to use.

In 2016, Russian company NtechLab caused a privacy scandal in the international media when it launched the FindFace face recognition system with the promise that Russian users could take photos of strangers in the street and link them to a social media profile on the social media platform Vkontakte (VK). In December 2017, Facebook rolled out a new feature that notifies a user when someone uploads a photo that includes what Facebook thinks is their face, even if they are not tagged. Facebook has attempted to frame the new functionality in a positive light, amidst prior backlashes. Facebook's head of privacy, Rob Sherman, addressed this new feature as one that gives people more control over their photos online. "We've thought about this as a really empowering feature," he says. "There may be photos that exist that you don't know about." Facebook's DeepFace has become the subject of several class action lawsuits under the Biometric Information Privacy Act, with claims alleging that Facebook is collecting and storing face recognition data of its users without obtaining informed consent, in direct violation of the 2008 Biometric Information Privacy Act (BIPA). The most recent case was dismissed in January 2016 because the court lacked jurisdiction. In the US, surveillance companies such as Clearview AI are relying on the First Amendment to the United States Constitution to data scrape user accounts on social media platforms for data that can be used in the development of facial recognition systems.

In 2019, the Financial Times first reported that facial recognition software was in use in the King's Cross area of London. The development around London's King's Cross mainline station includes shops, offices, Google's UK HQ and part of St Martin's College. According to the UK Information Commissioner's Office: "Scanning people's faces as they lawfully go about their daily lives, in order to identify them, is a potential threat to privacy that should concern us all." The UK Information Commissioner Elizabeth Denham launched an investigation into the use of the King's Cross facial recognition system, operated by the company Argent. In September 2019 it was announced by Argent that facial recognition software would no longer be used at King's Cross. Argent claimed that the software had been deployed between May 2016 and March 2018 on two cameras covering a pedestrian street running through the centre of the development. In October 2019, a report by the deputy London mayor Sophie Linden revealed that in a secret deal the Metropolitan Police had passed photos of seven people to Argent for use in their King's cross facial recognition system.

Automated Facial Recognition was trialled by the South Wales Police on multiple occasions between 2017 and 2019. The use of the technology was challenged in court by a private individual, Edward Bridges, with support from the charity Liberty (case known as R (Bridges) v Chief Constable South Wales Police). The case was heard in the Court of Appeal and a judgement was given in August 2020. The case argued that the use of Facial Recognition was a privacy violation on the basis that there was insufficient legal framework or proportionality in the use of Facial Recognition and that its use was in violation of the Data Protection Acts 1998 and 2018. The case was decided in favour of Bridges and did not award damages. The case was settled via a declaration of wrongdoing. In response to the case, the British Government has repeatedly attempted to pass a Bill regulating the use of Facial Recognition in public spaces. The proposed Bills have attempted to appoint a Commissioner with the ability to regulate Facial Recognition use by Government Services in a similar manner to the Commissioner for CCTV. Such a Bill has yet to come into force [correct as of September 2021].

In January 2023, New York Attorney General Letitia James asked for more information on the use of facial recognition technology from Madison Square Garden Entertainment following reports that the firm used it to block lawyers involved in litigation against the company from entering Madison Square Garden. She noted such a move would could go against federal, state, and local human rights laws.

As of 2018, it is still contested as to whether or not facial recognition technology works less accurately on people of color. One study by Joy Buolamwini (MIT Media Lab) and Timnit Gebru (Microsoft Research) found that the error rate for gender recognition for women of color within three commercial facial recognition systems ranged from 23.8% to 36%, whereas for lighter-skinned men it was between 0.0 and 1.6%. Overall accuracy rates for identifying men (91.9%) were higher than for women (79.4%), and none of the systems accommodated a non-binary understanding of gender. It also showed that the datasets used to train commercial facial recognition models were unrepresentative of the broader population and skewed toward lighter-skinned males. However, another study showed that several commercial facial recognition software sold to law enforcement offices around the country had a lower false non-match rate for black people than for white people.

Experts fear that face recognition systems may actually be hurting citizens the police claims they are trying to protect. It is considered an imperfect biometric, and in a study conducted by Georgetown University researcher Clare Garvie, she concluded that "there's no consensus in the scientific community that it provides a positive identification of somebody." It is believed that with such large margins of error in this technology, both legal advocates and facial recognition software companies say that the technology should only supply a portion of the case - no evidence that can lead to an arrest of an individual. The lack of regulations holding facial recognition technology companies to requirements of racially biased testing can be a significant flaw in the adoption of use in law enforcement. CyberExtruder, a company that markets itself to law enforcement said that they had not performed testing or research on bias in their software. CyberExtruder did note that some skin colors are more difficult for the software to recognize with current limitations of the technology. "Just as individuals with very dark skin are hard to identify with high significance via facial recognition, individuals with very pale skin are the same," said Blake Senftner, a senior software engineer at CyberExtruder.

The United States' National Institute of Standards and Technology (NIST) carried out extensive testing of FRT system 1:1 verification and 1:many identification. It also tested for the differing accuracy of FRT across different demographic groups. The independent study concluded at present, no FRT system has 100% accuracy.

In 2010, Peru passed the Law for Personal Data Protection, which defines biometric information that can be used to identify an individual as sensitive data. In 2012, Colombia passed a comprehensive Data Protection Law which defines biometric data as senstivite information. According to Article 9(1) of the EU's 2016 General Data Protection Regulation (GDPR) the processing of biometric data for the purpose of "uniquely identifying a natural person" is sensitive and the facial recognition data processed in this way becomes sensitive personal data. In response to the GDPR passing into the law of EU member states, EU based researchers voiced concern that if they were required under the GDPR to obtain individual's consent for the processing of their facial recognition data, a face database on the scale of MegaFace could never be established again. In September 2019 the Swedish Data Protection Authority (DPA) issued its first ever financial penalty for a violation of the EU's General Data Protection Regulation (GDPR) against a school that was using the technology to replace time-consuming roll calls during class. The DPA found that the school illegally obtained the biometric data of its students without completing an impact assessment. In addition the school did not make the DPA aware of the pilot scheme. A 200,000 SEK fine (€19,000/$21,000) was issued.[citation needed ]

In the United States of America several U.S. states have passed laws to protect the privacy of biometric data. Examples include the Illinois Biometric Information Privacy Act (BIPA) and the California Consumer Privacy Act (CCPA). In March 2020 California residents filed a class action against Clearview AI, alleging that the company had illegally collected biometric data online and with the help of face recognition technology built up a database of biometric data which was sold to companies and police forces. At the time Clearview AI already faced two lawsuits under BIPA and an investigation by the Privacy Commissioner of Canada for compliance with the Personal Information Protection and Electronic Documents Act (PIPEDA).

In May 2019, San Francisco, California became the first major United States city to ban the use of facial recognition software for police and other local government agencies' usage. San Francisco Supervisor, Aaron Peskin, introduced regulations that will require agencies to gain approval from the San Francisco Board of Supervisors to purchase surveillance technology. The regulations also require that agencies publicly disclose the intended use for new surveillance technology. In June 2019, Somerville, Massachusetts became the first city on the East Coast to ban face surveillance software for government use, specifically in police investigations and municipal surveillance. In July 2019, Oakland, California banned the usage of facial recognition technology by city departments.

The American Civil Liberties Union ("ACLU") has campaigned across the United States for transparency in surveillance technology and has supported both San Francisco and Somerville's ban on facial recognition software. The ACLU works to challenge the secrecy and surveillance with this technology.[citation needed ]

The West Lafayette, Indiana City Council passed an ordinance banning facial recognition surveillance technology.

The European " Reclaim Your Face" coalition launched in October 2020. The coalition calls for a ban on facial recognition and launched a European Citizens' Initiative in February 2021. More than 60 organizations call on the European Commission to strictly regulate the use of biometric surveillance technologies.

In the 18th and 19th century, the belief that facial expressions revealed the moral worth or true inner state of a human was widespread and physiognomy was a respected science in the Western world. From the early 19th century onwards photography was used in the physiognomic analysis of facial features and facial expression to detect insanity and dementia. In the 1960s and 1970s the study of human emotions and its expressions was reinvented by psychologists, who tried to define a normal range of emotional responses to events. The research on automated emotion recognition has since the 1970s focused on facial expressions and speech, which are regarded as the two most important ways in which humans communicate emotions to other humans. In the 1970s the Facial Action Coding System (FACS) categorization for the physical expression of emotions was established. Its developer Paul Ekman maintains that there are six emotions that are universal to all human beings and that these can be coded in facial expressions. Research into automatic emotion specific expression recognition has in the past decades focused on frontal view images of human faces.

In 2016, facial feature emotion recognition algorithms were among the new technologies, alongside high-definition CCTV, high resolution 3D face recognition and iris recognition, that found their way out of university research labs.[citation needed ] In 2016, Facebook acquired FacioMetrics, a facial feature emotion recognition corporate spin-off by Carnegie Mellon University. In the same year Apple Inc. acquired the facial feature emotion recognition start-up Emotient. By the end of 2016, commercial vendors of facial recognition systems offered to integrate and deploy emotion recognition algorithms for facial features.[citation needed ] The MIT's Media Lab spin-off Affectiva by late 2019 offered a facial expression emotion detection product that can recognize emotions in humans while driving.

Given that big data companies have much more funding than privacy researchers, it is very difficult for anti-facial recognition systems to keep up. There is also no guarantee that obfuscation techniques that were used for images taken in the past and stored, such as masks or software obfuscation, would protect users from facial-recognition analysis of those images by future technology.

In January 2013, Japanese researchers from the National Institute of Informatics created 'privacy visor' glasses that use nearly infrared light to make the face underneath it unrecognizable to face recognition software that use infrared. The latest version uses a titanium frame, light-reflective material and a mask which uses angles and patterns to disrupt facial recognition technology through both absorbing and bouncing back light sources. However, these methods are used to prevent infrared facial recognition and would not work on AI facial recognition of plain images. Some projects use adversarial machine learning to come up with new printed patterns that confuse existing face recognition software.

One method that may work to protect from facial recognition systems are specific haircuts and make-up patterns that prevent the used algorithms to detect a face, known as computer vision dazzle. Incidentally, the makeup styles popular with Juggalos may also protect against facial recognition.

Facial masks that are worn to protect from contagious viruses can reduce the accuracy of facial recognition systems. A 2020 NIST study, tested popular one-to-one matching systems and found a failure rate between five and fifty percent on masked individuals. speculated that the accuracy rate of mass surveillance systems, which were not included in the study, would be even less accurate than the accuracy of one-to-one matching systems. The facial recognition of Apple Pay can work through many barriers, including heavy makeup, thick beards and even sunglasses, but fails with masks. However, facial recognition of masked faces is increasingly getting more reliable.

Another solution is the application of obfuscation to images that may fool facial recognition systems while still appearing normal to a human user. These could be used for when images are posted online or on social media. However, as it is hard to remove images once they are on the internet, the obfuscation on these images may be defeated and the face of the user identified by future advances in technology. Two examples of this technique, developed in 2020, are the ANU's 'Camera Adversaria' camera app, and the University of Chicago's Fawkes image cloaking software algorithm which applies obfuscation to already taken photos. However, by 2021 the Fawkes obfuscation algorithm had already been specifically targeted by Microsoft Azure which changed its algorithm to lower Fawkes' effectiveness.

Farokhi, Sajad; Shamsuddin, Siti Mariyam; Flusser, Jan; Sheikh, U.U; Khansari, Mohammad; Jafari-Khouzani, Kourosh (2014). "Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform". Digital Signal Processing. 31 (1): 13-27. doi:10.1016/j.dsp.2014.04.008.

"The Face Detection Algorithm Set to Revolutionize Image Search" (Feb. 2015), MIT Technology Review

Garvie, Clare; Bedoya, Alvaro; Frankle, Jonathan (October 18, 2016). Perpetual Line Up: Unregulated Police Face Recognition in America. Center on Privacy & Technology at Georgetown Law.

Press, Eyal, "In Front of Their Faces: Does facial-recognition technology lead police to ignore contradictory evidence?", The New Yorker, 20 November 2023, pp. 20-26.

# data[].attributes.attachment_link.description
A facial recognition system is a technology potentially capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, and works by pinpointing and measuring facial features from a given image.

# data[].attributes.body
Main idea:
Facial recognition is the fastest way to enter into your device.
Arguments/facts:

- In only 5 seconds this technoly recognizes you and opens your phone
Dialog:

About a 99.44% of the faces are detected in only 5 seconds.
Does a tablet have the same facial recognition software as a tablet?
Summary:

This technology has not only turned into the easiest if not into the fastest. It only takes 5 seconds to enter into your phone. Isn't it amazing? It has been demostrated that about a 99.4% of faces of people which use this software have been detected in less than 10 seconds.
APA:
Thales group (2021, June 24)Facial recognition: top 7 trends (tech, vendors, markets, use cases). Retrieved from https://www.thalesgroup.com/en/markets/digital-identity-and-security/government/biometrics/facial-recognition

# data[].attributes.attachment_link.readable_text
The feature common to all these disruptive technologies is Artificial Intelligence (A.I.) and, more precisely, deep learning, where a system can learn from data.

Why is it important?

It's a central component of the latest-generation algorithms developed by Thales and other key players. It holds the secret to face detection, face tracking, face match, and real-time translation of conversations.

The result?

Face recognition systems are getting better all the time.

According to a recent NIST report, massive gains in recognition accuracy have been made in the last five years (2013- 2018) and exceed the 2010-2013 period.

Most of the face recognition algorithms in 2018 outperformed the most accurate algorithm from late 2013.

In its 2018 test, NIST found that 0.2% of searches in a database of 26.6 million photos failed to match the correct image, compared with a 4% failure rate in 2014.

There's more.

In NIST's 2020 tests, the best facial identification algorithm has an error rate of 0.08% - that's less than one error for 1.000 images. (source: How accurate are facial recognition systems, CSIS)

Yes, you understand that, right?

Think about it this way:

Artificial neural network algorithms are helping face recognition algorithms to be more accurate.

​

A study published in June 2019 estimates that by 2024, the global facial recognition market would generate $7billion of revenue, supported by a compound annual growth rate (CAGR) of 16% over 2019-2024.

For 2019, the market was estimated at $3.2 billion.

The two most significant drivers of this growth are surveillance in the public sector and numerous other applications in diverse market segments.

According to the study, the top facial recognition vendors include :

Accenture, Aware, BioID, Certibio, Fujitsu, Fulcrum Biometrics, Thales, HYPR, Idemia, Leidos, M2SYS, NEC, Nuance, Phonexia, and Smilepass.

The main facial recognition applications can be grouped into three principal categories.

Here are the top three application categories where facial recognition is being used.
Forensic specialists can use Automated Biometric Identification Systems (ABIS) to compare multiple types of biometrics.
This market is led by increased activity to combat crime and terrorism.

The benefits of facial recognition systems for policing are evident: detection and prevention of crime.

Facial recognition is used when issuing identity documents and, most often, combined with other biometric technologies such as fingerprints (preventing I.D. fraud and identity theft).

Face match is used at border checks to compare the portrait on a digitized biometric passport with the holder's face. In 2017, Thales supplied the new automated control gates for the PARAFE system (Automated Fast Track Crossing at External Borders) at Roissy Charles de Gaulle Airport in Paris. This solution has been devised to facilitate the evolution from fingerprint recognition to facial recognition in 2018.

Face biometrics can also be employed in police checks, although it is rigorously controlled in Europe. In 2016, the "man in the hat" responsible for the Brussels terror attacks was identified thanks to FBI facial recognition software. The South Wales Police implemented it at the UEFA Champions League Final 2017.

In the United States, 26 states (and probably as many as 30) allow law enforcement to run searches against their databases of driver's licenses and I.D. photos. The FBI has access to driver's license photos from 18 states.

​Drones and aerial cameras offer an exciting combination of facial recognition applied to large areas during mass events. According to the Keesing Journal of Documents and Identity of June 2018, some hovering drone systems can carry a 10-kilo camera lens that can identify a suspect from 800 meters to a height of 100 meters. The drone can be connected to the ground via a power cable with an unlimited power supply. The communication to ground control can't be intercepted as it also uses a line.

Facial recognition CCTV systems can improve performance in carrying out public security missions. Let's illustrate this with four examples:

1. Find missing children and disoriented adults.

2. Identify and find exploited children.

3. Identify and track criminals.

4. Support and accelerate investigations.

2. Health

Significant advances have been made in this area.

Thanks to deep learning and face analysis, it is already possible to:

track a patient's use of medication more accurately

detect genetic diseases such as DiGeorge syndrome with a success rate of 96.6%

support pain management procedures.

3. Banking and retail

This area is undoubtedly the one where facial recognition was least expected. And yet, quite possibly, it promises the most.

Know Your Customer ( KYC) with facial recognition online will be a hot topic in 2021.

Why?

Because 64% of primary checking account openings were done online in Q2 2020 ( and 36% in branches) in the United States alone.

The pandemic has accelerated this emerging dynamic, and many branches are temporarily closed.

Besides, increased mobile usage urges businesses to focus on mobile-first and develop fully mobile user-friendly onboarding experiences.

During the selfie process, the technology shall provide a liveness detection to avoid fraud using a static image.

Liveness detection proves that the selfie taken comes from a live person.

The result?

Adapting to current customer preferences, financial institutions (F.I.s) invest in digital onboarding through online and mobile channels.
Facial recognition with liveness detection simplifies online onboarding and KYC procedures. Thales is a major provider of identity verification solutions, including this feature.
According to Forbes, digital account opening (DAO) was the most popular technology in banking for the third consecutive year. Nearly 80% of financial institutions add new DAO systems or enhance existing ones in 2020 and 2021.

This important trend is combined with the latest marketing advances in customer experience.

By placing cameras in retail outlets, it is now possible to analyze shoppers' behavior and improve the customer purchase process.

How exactly?

Like the system recently designed by Facebook, sales staff are provided with customer information taken from their social media profiles to produce expertly customized responses.

The American department store Saks Fifth Avenue is already using such a system. Amazon Go stores are reportedly using it.

How long before the selfie payment?

Since 2017, KFC, the American king of fried chicken, and Chinese retail and tech giant Alibaba have been testing a face recognition payment solution in Hangzhou, China.

In March 2021, 52 Perekrestok stores (Перекрёсток) from X5 retail group launched touchless payment by face for self-service checkout terminals with Visa Payment System and Sberbank.

The facial recognition payment system would be used in 3,000 stores by yearend, according to Yahoo!

There's more.

According to Interfax, muscovites can pay for metro rides at the end of 2021.

#4 Mapping of new users

While the United States currently offers the largest market for face recognition opportunities, the Asia-Pacific region sees the fastest growth in the sector. China and India lead the field.

Face recognition in China

Face recognition technology is the new hot topic in China, from banks and airports to police.

Now authorities are expanding the facial recognition sunglasses program as police are beginning to use them in Beijing's outskirts.

China is also setting up and perfecting a video surveillance network countrywide.

According to CNBC, over 200 million surveillance cameras were used in 2018; over 500 million are expected by 2021.

The facial recognition towers in Chinese cities are emblematic of this move.

This is linked to the social credit system the Chinese government is developing.

In the TOP 10 cities with the most street cameras per person, Chongqing, Shenzhen, Shanghai, Tianjin, and Ji'nan lead the pack.

London is #6 and Atlanta #10, according to the Guardian of 2 December 2019.

There's more.

Chinese police are working with artificial intelligence companies such as Yitu, Megvii (in partnership with Huawei), SenseTime, and CloudWalk, according to The New York Times on 14 April 2019.

China's ambitions in A.I. (and facial recognition technology) are high. The country aims to become a world leader in A.I. by 2030.

Surprisingly, China provides strong biometric data protection against private entities AND increases the government's access to personal information.

This paradox is evidenced by privacy expert Emmanuel Pernot- Leplay's report dated 2 November 2020.

Facial recognition in Asia

Facial recognition will be an important topic for the 2020 Olympic Games in Tokyo (postponed to September 2021).

This technology will automatically identify authorized persons and grant them access, enhancing their experience and safety. It's also being used in Japan for easier mobile banking access.

In Sydney, face recognition is undergoing trials at airports to help move people through security much faster and safer.

In India, the Aadhaar project is the largest biometric database in the world. It already provides a unique digital identity number to 1.29 billion residents as of the end of March 2021.

UIDAI, the authority in charge, announced that facial authentication would be launched in a phased roll-out.

It's presently being tested for financial services ( October 2020.)

Face authentication will be available as an add-on service in fusion mode and another authentication factor like a fingerprint, Iris, or TOTP.

India could also roll out the world's most extensive face recognition system in 2021.

The National Crime Records Bureau ( NCRB) has issued an RFP inviting bids to develop a nationwide facial recognition system.

According to the 160-page document, the system will be a centralized web application hosted at the NCRB Data Center in Delhi. It will be available for access to all the police stations.

It will automatically identify people from CCTV videos and images. The Bureau states it will help police catch criminals, find missing people, and identify dead bodies.

Other large projects

The Superior Electoral Court ( Tribunal Superior Eleitoral) is involved in Brazil's nationwide biometric data collection project. The aim is to create a biometric database and unique I.D. cards, recording the information of 140 million citizens.

In Africa, Gabon, Cameroon, and Burkina Faso have chosen Thales to meet the challenges of biometric identity to identify voters uniquely.

Russia's Central Bank has been deploying a countrywide program since 2017 designed to collect faces, voices, iris scans, and fingerprints.

But the process is progressing very slowly, according to the Biometricupdate website of 13 March 2019.

Moscow claims one of the world's largest networks of 160,000 surveillance cameras by the end of 2019 and is fitted with facial recognition technology for public safety.

The roll-out started in January 2020.

Russian law does not regulate non-consensual face detection and analysis.

#5 When face recognition strengthens the legal system

Facial recognition technologies radically affect the ethical and societal challenges data protection poses.

Do these technological feats, worthy of science-fiction novels, genuinely threaten our freedom?

And with it, our anonymity?

E.U. and U.K. biometric data protection

The General Data Protection Regulation (GDPR) provides a rigorous framework for these practices in Europe and the U.K.

Any investigations into a citizen's private life or business travel habits are out of the question, and any such invasions of privacy carry severe penalties.

Applicable from May 2018, the GDPR supports the principle of a harmonized European framework, particularly protecting the right to be forgotten and giving consent through explicit affirmative action.

Yes, you read it well. There's now one law for 500 million people.

This directive is bound to have international repercussions.

U.S. biometric data protection landscape

Without a federal law, cities and states are filling the gap.

The State of Washington was the third U.S. state (after Illinois and Texas) to formally protect biometric data through a new law introduced in June 2017.

California was the fourth state as of January 2020.

The California Consumer Privacy Act (), passed in June 2018 and effective as of 1 January 2020, will severely impact CCPA privacy rights and consumer protection for residents of California and the whole nation.

The law is frequently presented as a model for a federal data privacy law.

In that sense, the CCPA can potentially become as consequential as the GDPR.

In July 2018, Bradford L. Smith, Microsoft's president, compared the face recognition technology to products like highly regulated medicines, and he urged Congress to study it and oversee its use.

In May 2019, U.S. Rep. Alexandria Ocasio-Cortez voiced her "absolute" concerns in a Committee Hearing on facial recognition Technology (Impact on our Civil Rights and Liberties).

A New York State law called the Stop Hacks and Improve Electronic Data Security ( SHIELD) became effective on 21 March 2020. It requires implementing a cybersecurity program and protective measures for N.Y. State residents.

The act applies to businesses that collect the personal information of N.Y. residents.

With the act, New York now stands beside California.

Facial recognition bans (San Francisco, Somerville, Oakland, San Diego, Boston, Portland)

Privacy and civil rights concerns have escalated in the country as face recognition gains traction as a law enforcement tool, and on 6 May 2019, San Francisco voted to ban facial recognition.

It is the first ban of its kind on the use of face recognition.

The anti-surveillance ordinance signed by San Francisco's Board of Supervisors bars city agencies, including San Francisco PD, from using the technology as of June 2019.

Yes, this includes law enforcement.

There's more.

As reported by the Boston Globe on 27 June 2019, the Somerville City Council (Massachusetts) voted to ban facial recognition, making the city the second community to make such a decision.

Lather, rinse, repeat.

On 16 July 2019, Oakland (California) took the same decision and became the third U.S. city to ban face recognition technology. It is interesting to note that the Oakland Police Department is not using this technology and was not planning to use it.

San Diego took the same decision at the end of December 2019 before the new Californian law. This new law (Assembly Bill 215) about facial recognition and other biometric surveillance) specifically prohibits the use of police body cameras in California. The ban is in place for three years as of 1 January 2020.

On 24 June 2020, Boston voted to ban face surveillance technology by police, as reported by the Boston Herald.

private stores. (Portland (Oregon) decided its ban on 9 September 2020 (effective 1 January 2021.) The city is the first to extend it to "private entities in places of public accommodation", such as CNN).

Massachusetts passed a reform bill in December 2020 restricting the use of facial recognition. It's applicable as of May 2021.

Virginia legislature passed (April 2021) a new bill (H.B. 2031) that prohibits law enforcement agencies from continuing to use facial recognition software after 1 July 2021.

Since the San Francisco, Sommerville, Oakland, and now San Diego, Boston, and Portland rulings, the debate has gotten louder in many cities and states, not just in the U.S.

In Europe, at the end of August 2019, Sweden's Data Protection Authority decided to ban facial recognition technology in schools. It fined a local high school (the first GDPR penalty in the country).

How to better regulate emerging technologies?

Should other cities or countries follow this example?

Is the ban just a "pause button" to better assess risks?

Is this a step backwards for public safety?

Is there a policy vacuum? At which level?

Stay tuned for the outcome of all these discussions as the U.S. Congress is getting pressure from activists to ban the technology and from providers) to regulate.

But there's still no Federal legal framework to address the issue as of May 2021.

The E.U. Commission plans to act on the indiscriminate use of facial identifier technology. The European Commission president Ursula von der Leyen wants a coordinated approach to artificial intelligence's human and ethical implications. She has pledged to publish an A.I. legislation blueprint very soon.

The final version of the European Commission whitepaper is available online. The European Commission presented tough draft rules in April 2021. But according to Reuters, it could take years before the regulations come into force.

Similarly, in June 2021, the E.U.'s two privacy watchdogs (EDPB and EDPS) called for a ban on facial recognition in publicly accessible spaces.

Again the questions of privacy, consent, and function creep (data collected for one purpose being used for another)are central to the debate.

In our biometric data dossier, find more on biometric data protection laws(E.U., U.K., and U.S. perspectives).

India and its national biometric identification scheme, Aadhaar

In India, thanks to the Puttaswamy judgment delivered on 27 August 2017, the Supreme Court has enshrined the right to privacy in the country's constitution. This decision has rebalanced the relationship between citizens and the state and posed a new challenge to expanding the Aadhaar project.

The Indian government, however, approved the use of the country's biometric EID program by private entities on 28 February 2019.

Rebound effect: the legal system and its professions get even stronger.

As ambassadors and guardians of data protection regulation, data protection officers have become necessary for businesses and have a much sought-after role.

#6 The rebels - facial recognition hackers

Despite this technical and legal arsenal designed to protect data, citizens, and their anonymity, critical voices have still been raised.

Some parties are concerned and alarmed by these developments. Some have taken action.

But can facial recognition be fooled?

Grigory Bakunov in Russia has invented a solution to escape proper face detection and confuse face detection devices. He has developed an algorithm that creates special makeup to fool the software. However, he has not brought his product to market after realizing how easily criminals could use it.

In Germany, Berlin artist Adam Harvey has developed a similar device known as CV Dazzle. He is now working on clothing featuring patterns to prevent detection. The hyperface camouflage includes patterns in fabric, such as eyes and mouths, to fool the face recognition system.

In late 2017, a Vietnamese company successfully used a mask to hack the Face ID face recognition function of Apple's iPhone X. However, the hack is too complicated to implement for large-scale exploitation.

Around the same time, researchers from a German company revealeda hack that allowed them to bypass the facial authentication of Windows 10 Hello by printing a facial image in infrared.

​Forbes announced in May 2018 that researchers from the University of Toronto had developed an algorithm to disrupt facial recognition software (aka privacy filter).

In August 2020, the Verge detailed a "cloaking" app named Fawkes. The software gradually distorts your selfies and other pics you may leave on social media. The tool comes from the University of Chicago's Sand Lab.

In short, a user could apply a filter that modifies​ specific pixels in an image before putting it on the web. These changes are imperceptible to the human eye but are very confusing for facial recognition algorithms.

In November 2020, a tool named Anonymizer was made available by Generated Media. The software creates a series of synthetic portraits from a picture you can upload. According to the website, the images are mathematically similar to your face and look like you but will trick facial recognition software. It could be an interesting solution to fool systems like Clearview A.I. that are scrapping millions of faces from social media (learn more on the Clearview A.I. controversy).

We tested Anonymizer on 27 November 2020. But the 40+ doppelgangers we got were far from looking like the original portrait uploaded.

An interesting experiment by Thomas Smith, published on 28 January 2021, revealed a simple technique to make you invisible.

According to his tests, wearing a disposable mask and opaque sunglasses is a powerful combination to render you invisible.

Why?

In that case, the F.R. systems are denied too much valuable information (mouth, nose, eyes, eyebrows) to make a precise facial comparison.

The industry is working on anti-spoofing mechanisms, and standardization groups have specifically identified two topics:

1. Make sure the captured image has been done from a person and not from a photograph (2D), a video screen (2D), or a mask (3D) (liveness check orliveness detection)

2. Ensure that two or more individuals' facial images (morphed portraits) have not been joined into a reference document, such as a passport.

#7 Further together - towards hybridized solutions

Future identification and authentication solutions will borrow from all aspects of biometrics.

This will lead to a biometric mix capable of guaranteeing total security and privacy for all stakeholders in the ecosystem.

It's very much the spirit of Thales Gemalto IdCloud Fraud Prevention, a risk assessment and fraud detection software for payments.

In this solution, geolocation, I.P. addresses (the device being used), and keying patter ns can create a solid combination to authenticate users for online banking or egovernment services securely.

This seventh trend belongs to us.

It's our job to envisage it together and make it happen through high-added-value biometric projects.

Thales has specialized in biometric technologies for almost 30 years. The company has always collaborated with the best research, ethics, and biometric application players.

Face recognition and you.

Now it's your turn.

The months hold many changes in store.

Indeed, we can't claim to predict all the essential topics that will emerge in the short-term future.

Can you fill in some of the gaps?

If you've something to say on facial recognition, tech, trends, a question to ask, or have found this article useful, please leave a comment in the box below.

We'd also welcome any suggestions on how it could be improved or proposals for future articles.
We look forward to hearing from you.

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Facial recognition: top 7 trends (tech, vendors, use cases)

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Facial recognition is a hot topic and somewhat controversial. Discover 7 trends likely to shape the face recognition landscape for the next 2 years.

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Facial Recognition Is Everywhere. Here’s What We Can Do About It.

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Facial Recognition Is Everywhere. Here’s What We Can Do About It.

# data[].attributes.body
Main idea:
Though most people use facial recognition to unlock their mobile phones or sort their photos, the way the government is using has a much greater impact on people's lives.

Arguments/facts:

A brief history of facial recognition:
Facial recognition was actually introduced in the 1960s when it was used to classify photos of people. But it was the 2010s that really kickstarted the modern era of facial recognition. It was when computers were finally powerful enough to train networks to find facial recognition a standard feature. Soon after, facial recognition was being used for FaceBook tagging and the identification of participants in protests...
There have been concerns about unsolicited facial recognition in public spaces. This is what Brenda Leong has to say about it “You just walk through a set of cameras and all those things happen very seamlessly, sports arenas, event venues, amusement parks, all those places either are using or would have ideas of ways to use it similarly.”

Dialogue:

Summary:
Though facial recognition might seem like quite a modern feature in our day to day lives, it has been around for more than 60 years.

APA:
Thorin Klosowski, (2020, July 15). Facial Recognition Is Everywhere. Here’s What We Can Do About It.
Retrieved from:

https://www.nytimes.com/wirecutter/blog/how-facial-recognition-works/

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Facial Recognition Is Everywhere. Here’s What We Can Do About It.

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You can opt out of facial recognition in some cases, but the history and future of the technology suggest we’ll need bigger solutions to its privacy problems.

# data[].attributes.body
how does it work?

Facial recognition is a way of recognizing a human face through technology. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. ... That's because facial recognition has many commercial applications.

# data[].attributes.attachment_link.title
What is facial recognition? How facial recognition works

# data[].attributes.attachment_link.description
Facial recognition is a way of recognizing a human face through technology. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match.

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Provides opportunities for fraud and other crimes

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Provides opportunities for fraud and other crimes

# data[].attributes.body
Main idea:
The innocent finish in prison because robbers make fake face recognition.

Arguments/facts:
For example, sneakers could perform after image quests on a photo taken in a public place to gather information about their victims and determine who they're and where they live.

Dialog:

Why do they put on prison innocents unless of real robbers?
Why police don't see the difference between the robbers with the innocents?

Summary:
Lawbreakers can also use facial recognition to commit crimes against innocent victims. They can collect particular information from individualities, including images and vids collected from facial reviews and stored in databases, to commit identity fraud. With this information, a robber could take out credit cards and other debts open bank accounts in the victim's name, or indeed produce an illegal record using the victim's identity. Beyond fraud, bad actors can kill or stalk victims using facial recognition technology.

APA:
David, G. (20 Jul 2021). The pros and cons of facial recognition technology. Retrieved from https://www.itpro.com/security/privacy/356882/the-pros-and-cons-of-facial-recognition-technology

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The pros and cons of facial recognition technology

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There are plenty of pros and cons of facial recognition technology, but is it really worth risking user privacy in the name of efficiency and security?

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Ban dangerous facial recognition technology that amplifies racist policing

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Ban dangerous facial recognition technology that amplifies racist policing

# data[].attributes.body
Main idea: Facial recognition violates privacy

Arguments/facts:

-They use this as a way to threaten because your face recognizes has information about you.
-China, UAE, North Korea, Iran, and Iraq are the countries where privacy is violated.

Dialog:
How does it violate privacy? How do they use it as a threat?

Summary: Facial recognition systems are a form of absolute violation of the right to privacy and undermine the right to freedom of expression and peaceful assembly. Facial recognition has problems because it is used as a weapon to threaten. Some countries with limited particular freedoms, generally use facial recognition to asset on citizens and arrest those supposed troublemakers.

APA: Amnestry International (January 26, 2021 8:22 am).

Ban dangerous facial recognition technology that amplifies racist policing. Retrieved from https://www.amnesty.org/en/latest/news/2021/01/ban-dangerous-facial-recognition-technology-that-amplifies-racist-policing/#:~:text=Facial%20recognition%20systems%20are%20a,marginalized%20communities%20around%20the%20world.

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Facial recognition is a form of mass surveillance that threatens human rights

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Summary:
As technology rapidly improves and becomes more efficient, facial recognition is without a doubt going to form a big part of our lives. Especially with big companies such as Meta and Apple actively using it, it will even be hard to avoid.

However, though it might all sound exciting and new, we must take privacy and social issues into account.

APA

# data[].attributes.attachment_link.authors[].name
Senior Vice President and Director, Strategic Technologies Program

# data[].attributes.attachment_link.description
Download the Report Concern over the misuse of facial recognition technology is one of the latest fears over technological change that have included Frankenfish, mass surveillance, chip implants, and artificial intelligence (AI). As with these earlier examples, there is both confusion and exaggerat…

# data[].attributes.headline
The pros and cons of facial recognition technology

# data[].attributes.subject
The pros and cons of facial recognition technology

# data[].attributes.body
Main idea:
Facial recognition has becomed really efficent when trying to find someone or protecting a buisness.
Arguments/facts:

-Helps find missing people and identify perpetrators
-Protects businesses against theft
Dialog:

How many people can facial recognition find in 15 seconds?
Is the money from buisnesses controlled by facial recognition?
Summary:

This modern tehnology finds missing people by comparing faces on a live camera. This can be really poitive when kids go missing, or even to find children which have been missing for years, by using a surface which predicts how they would look when being older.  Can be really effective when potecting a buisness software or by tracking the thieves who  have stolen.
APA:
David, G. (2021, July 20). The pros and cons of facial recognition technology. Retrieved from https://www.itpro.com/security/privacy/356882/the-pros-and-cons-of-facial-recognition-technology

# data[].attributes.attachment_link.title
The pros and cons of facial recognition technology

# data[].attributes.attachment_link.description
There are plenty of pros and cons of facial recognition technology, but is it really worth risking user privacy in the name of efficiency and security?

# data[].attributes.body
Dialogue:
- Is it legal to take pictures of strangers on the street to be able to identify them?
- Is facial recognition racially biased?
- Are there any major risks with facial recognition?
- What is the accuracy of facial recognition?

Summary:

APA

# data[].attributes.attachment_link.authors[].name
Senior Vice President and Director, Strategic Technologies Program

# data[].attributes.attachment_link.description
Download the Report Concern over the misuse of facial recognition technology is one of the latest fears over technological change that have included Frankenfish, mass surveillance, chip implants, and artificial intelligence (AI). As with these earlier examples, there is both confusion and exaggerat…

# data[].attributes.headline
The ethical questions that haunt facial-recognition research

# data[].attributes.subject
The ethical questions that haunt facial-recognition research

# data[].attributes.body
Arguments/facts:
Since the recent rise in the popularity of human recognition in the past few years, there have been many ethical controversies behind it. While facial recognition can help verify someone's identity, it also raises privacy issues.
How does it work?
Recognising faces might sound like quite an easy task. You're familiar with a family, friend or acquaintance's face. That's how facial recognition works, but on a much greater scale:
1. An image of your face is taken.
2. Your face's geometry is identified.
3. Your facial signature is compared to other known faces.
4. A determination is made.
Facts:

- Some facial recognition tools have trouble with racial bias: According to the researchers, facial recognition technologies falsely identified Black and Asian faces 10 to 100 times more often than they did white faces.
- Facial recognition tools are versatile and can identify objects.
- Provides an added level of security to the customer's experience.
- Potential to streamline many aspects of everyday life (e.g. identify threats, simplify travel, diagnose conditions).
- Facial recognition is still in its nascent years and is yet to evolve.
- It is almost impossible to escape facial recognition: as we post our faces on social media we are willingly give all the needed information to the AI behind the technology.
-

Dialog:

Summary:

APA

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Facial recognition is something which is evolving very quickly. As users of this technology we should know facts about it in depth. Usually these factors are not highlighted in a facial recognition system. Over the past few years, facial recognition technology has been more ubiquitous than ever.

Mid- to high-range phones are now equipped with face recognition to unlock phones, adding a security layer to safeguard personal and sensitive data. Facebook also uses the technology to identify members who appear on photos, allowing users to find photos they're in and get suggestions on who to tag and many more features.

But there are some facts that we should know about facial recognition systems.

Facial recognition has broken free from the realms of science fiction and made its way to the mainstream. But beyond unlocking phones and tagging photos on social media, facial recognition proves useful in other real-world applications.
Here are those list of 10 facts about the same
1) Reliance on probability and racial bias are serious challenges

Facial recognition software operates based on probabilities . Unfortunately, some facial recognition tools have problems with racial bias. CBS News reported on this problem in June 2020, and it is far from solved. As noted in the CBS story, as a result of concerns about racial bias IBM has announced that it is abandoning facial recognition research.

2) It has its own good and bad according to data trained on

Facial recognition is an AI-based technology, which means it is only as good as the data it was trained on. As such it is naturally inclined to develop biases.

To ensure ethical and fair use of AI, leaders should make use of best practices and technologies to mitigate the risks, including wide data sets, proactive bias detection, end-to-end visibility and transparency regarding the health of these models. There are biases and loops. This is a secret buried that no one brings into light.

3) Facial recognition tools can also recognise objects

The tools being used for facial recognition are much more versatile than people think. Many can also be used for object recognition. This means these algorithms can recognise and categorise things like cars, trees, purses, colours really anything you can think of. Also it does so at a tremendous scale. There is separate data fed for this in facial recognition systems.

4) It can make the customer experience more secure

Facial recognition technology provides tremendous value by bringing an added level of security to the customer experience, but regulations can limit the scope and capacity in which brands can use it.

Companies and consumers alike should educate themselves about applicable data privacy laws, as they vary by country, and businesses must be transparent about how captured data will be used and/or stored. Customer satisfaction and trust is everything for a company after all.

5) It has the potential to streamline many aspects of daily life

Facial recognition technology examines facial features to recognise and verify individuals. Although there are serious limitations, policy issues and privacy concerns that need to be addressed, the technology has the potential to help simplify travel, streamline financial transactions, diagnose conditions, identify threats and provide identification to a billion people globally who currently lack it.

There are threats and bias, awareness regarding it is quite less in general users of facial recognition systems.

6) Just a point of authentication data

What many people fail to understand is that facial recognition is just one data point of authentication. Highly secure access takes place via a multi factor authentication model that doesn't rely on only one form of ID. Therefore, facial recognition isn't necessarily "foolproof."

Facial spoofs are becoming more common, so don't assume facial recognition can't be hacked or bypassed. People have to ensure their privacy with facial recognition systems. So, they should focus on learning to use it more smartly and carefully.

7) This system is still very new and evolving

Facial recognition is in its nascent stages. There is so much more work to be done. Don't trust and believe when industry leaders tell you that we are in advanced stages we have yet to get there. Two other areas are yet to be understood, the safety and security of solutions based on facial recognition.

Be careful about what you are doing, how you are doing it and if you are storing any data. This is a new technology and there are amendments to it, it's not completely safe or smart. Believing or not is a crucial factor here. In this generation of hackers and viruses nothing is foolproof.

8) It frequently gives a little poor performance

Facial recognition technology can have a wide-ranging efficacy. Sometimes it can outperform a human, and sometimes it does very poorly because of small factors like light or shade or how the image is captured. It certainly will get better, but right now the tech seems similar to polygraphs in that people put a lot of faith into it even though it can frequently be wrong.

We all have faced such issues in this system, it's still being developed. Make sure you have a very good facial recognition system setup like Duty Par is a very efficient and gives a good performance

9) It is almost impossible to escape facial recognition

Posting our photos on the internet has long become our habit. Which means that we willingly give all the needed information to the AI behind the technology. Given this notion, anonymity is becoming a thing of the past, and we have to make our peace with the fact that broad use of facial recognition will soon become a new normal for all of us.

There is no term like private life to this generation of social media and influencers and AI will be just as normal as necessity to us in upcoming years. Studies have bought to our knowledge that AI is the future for humans (As explained in this YouTube video below by CNET).

10) Accuracy of Facial Recognition System

While the use of 3D images has improved the accuracy of facial recognition systems, the risk of false positives (wrong person is identified) or false negatives (a person who should have been identified is not) remains. The accuracy of the match can be impacted by such things as the quality of the image, changes that occur to faces over time and the quality of the algorithm used.

When using facial recognition for identification, the system will search for similar images. This basically places individuals in a "digital line-up". In general, facial recognition software is complemented by manual review of matches when such issues arise. It is said to be quite accurate though.

Conclusion:

Facial recognition technology has come a long way in the last twenty years. Today, machines are able to automatically verify identity information for secure transactions, for surveillance and security tasks, and for access control to buildings etc. We'll see in upcoming years what more facial recognition has to offer to mankind.

Some of the related articles:

1. How does facial recognition system works

2. Different features in facial recognition system

References:

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10 Facts About Facial Recognition System | DutyPar

# data[].attributes.attachment_link.description
Facial recognition is something which is evolving very quickly. As users of this technology we should know facts about it in depth. Usually these factors are not highlighted in a facial recognition system. Over the past few years, facial recognition technology has been more ubiquitous than ever.

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Philosophers Lead Academics’ Effort To Restrict Facial Recognition Technology

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Philosophers Lead Academics’ Effort To Restrict Facial Recognition Technology

# data[].attributes.body
Main idea:
Amazon knows just by the facial recognition who is going to buy.

Arguments/facts:
- Depending on who is buying less or more price.
- Depending on who is buying the different products are shown.

Dialog:
How Amazon can detect face recognition what do they like?
Amazon with face recognition know everything you like?
Does Amazon save what you prefer buying?

Summary:

Someone from the Amazon company stated that they can recognize in a few seconds who is the person who is going to compare on Amazon only by face recognition. Amazon offers a "follow people" function that, depending on who they see through face recognition, recommends one thing or another. And according to your face-recognizing, they put things with more or less price.

APA:
Justin W. (June 28, 2018 at 12:10 pm).

Philosophers Lead Academics’ Effort To Restrict Facial Recognition Technology. Retrieved from https://dailynous.com/2018/06/28/philosophers-lead-academics-effort-restrict-facial-recognition-technology/

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If you're like most people, you probably haven't been thinking much about facial recognition technology. Philosopher Evan Selinger (Rochester Institute of Technology), has, and he thinks we all should be, too, for it poses a serious threat to human welfare. Now he, Peter Asaro (a philosopher at The New School), and others have written an open letter to Amazon CEO Jeff Bezos and Amazon's executives in charge of artificial intelligence objecting to the firm providing its facial recognition technology to the government.

Hardly a Luddite or a proponent of the (in my view hopeless) precautionary principle, Selinger knows that some dangerous technologies are sufficiently beneficial, and so "are worth preserving with rules that mitigate harm but accept reasonable levels of risk." However, he and Woodrow Hartzog (Northeastern) argue:
Facial recognition systems are not among these technologies. They can't exist with benefits flowing and harms adequately curbed. That's because the most-touted benefits of facial recognition would require implementing oppressive, ubiquitous surveillance systems and the kind of loose and dangerous data practices that civil rights and privacy rules aim to prevent. Consent rules, procedural requirements, and boilerplate contracts are no match for that kind of formidable infrastructure and irresistible incentives for exploitation.
The technology is becoming more widespread, with one of its most prominent examples being Amazon's " Rekognition."*Amazon has been providing government agencies with Rekognition, prompting objections from the American Civil Liberties Union (ACLU):
Rekognition is a powerful surveillance system readily available to violate rights and target communities of color. Amazon states that Rekognition can identify people in real-time by instantaneously searching databases containing tens of millions of faces. Amazon offers a "person tracking" feature that it says "makes investigation and monitoring of individuals easy and accurate" for "surveillance applications." Amazon says Rekognition can be used to identify "all faces in group photos, crowded events, and public places such as airports"-at a time when Americans are joining public protests at unprecedented levels. Amazon also encourages the use of Rekognition to monitor "people of interest," raising the possibility that those labeled suspicious by governments-such as undocumented immigrants or Black activists-will be targeted for Rekognition surveillance. Amazon has even advertised Rekognition for use with officer body cameras, which would fully transform those devices into mobile surveillance cameras aimed at the public.
People should be free to walk down the street without being watched by the government. Facial recognition in American communities threatens this freedom. In overpoliced communities of color, it could effectively eliminate it. The federal government could use this facial recognition technology to continuously track immigrants as they embark on new lives. Local police could use it to identify political protesters captured by officer body cameras. With Rekognition, Amazon delivers these dangerous surveillance powers directly to the government.

As Selinger and Hartzog note, surveillance via facial recognition technology is itself oppressive, but is also a tool the government is likely to use (or allow to be used) to harm people and violate rights through, for example, "rampant, nontransparent, targeted drone strikes; overreaching social credit systems that exercise power through blacklisting; and relentless enforcement of even the most trivial of laws, like jaywalking and failing to properly sort your garbage cans."

They also think that "technology creep," an idea developed by Selinger and Brett Frishchmann (Villanova) in Re-Engineering Humanity, is a reasonable worry to have regarding this technology:

once the infrastructure for facial recognition technology grows to a certain point, with advances in machine learning and A.I. leading the way, its use will become so normalized that a new common sense will be formed. People will expect facial recognition technology to be the go-to tool for solving more and more problems, and they'll have a hard time seeing alternatives as anything but old-fashioned and outdated. This is how "techno-social engineering creep" works.

In an open letter to Amazon executives, Asaro, Selinger, Hartzog, and others call for the firm to:

Stop supplying government and law enforcement agencies with facial recognition technology.

Cancel its cloud services contracts with Palantir and other intermediary companies that provide data analytics and AI services to police or militaries.

Establish ethical guidelines that prohibit the weaponization and militarization of this and other technologies that threaten privacy, civil and human rights, and establish transparency and accountability mechanisms to ensure those guidelines are implemented.

You can read the rest of the letter and add your name to the list of signatories here.

# data[].attributes.attachment_link.title
Philosophers Lead Academics' Effort To Restrict Facial Recognition Technology | Daily Nous

# data[].attributes.attachment_link.description
If you're like most people, you probably haven't been thinking much about facial recognition technology. Philosopher Evan Selinger (Rochester Institute of Technology), has, and he thinks we all should be, too, for it poses a serious threat to human welfare.

# data[].attributes.body
Main idea:
Facial recognition has becomed an essencial technlogy in our daily lives. It has becomed the fastest and safest way of checking identities.
Arguments/facts:

- The  fastest way of entering into our devices.
- ID or a Ticket have  been exchanged for facial recognition in the train estation of Osaka.
- The easiest way for police when identifying a suspect after a crime
Dialog:

Is facial recognition really expensive?
What is the percentage of people using facial recognitio in their devices?
Should facial recognition be used in schools to identify who enters and gets out of school?
Summary:

Facial recogition is a type of technology which now a days has becomed really useful. About a 59% of people trust on biometric identification, since it is the most efficient and fastest way of entering into our divices. It has also become extremely important  in terms of security, since scanning peoples faces when entering into, trains, plains, etc... can be really helpful to control the security in those. Lets not forget about how this technology can help police officers, having the face of a suspect  which could have comitted a crime, is really useful when attempting to find and interogate the person.
APA:
Nikolett, A. & Alice, N. (2021, August 03).

Facial Recognition: Pros And Cons. Retrieved from https://www.liberties.eu/en/stories/pros-and-cons-of-facial-recognition/43708

# data[].attributes.attachment_link.readable_text
Facial recognition technology is a hot topic, currently attracting heated debates worldwide. On the one hand, facial recognition technology is seen as highly efficient. It is fast and (argued to be) objective in performing tasks. On the other hand, there is a concern that the biases and inaccuracies inherent in these systems might lead to discrimination and jeopardise fundamental human rights, such as our freedom to protest against measures we disagree with.

What is facial recognition and how does it work?

The meaning of facial recognition is perhaps intuitive, but it won't harm to delve into some detail about what the technology really entails. The UK's Information Commissioner's Office defines facial recognition as "the process by which a person can be identified or otherwise recognized from a digital image." Just like an ID card, your face can serve as a proof of identity.

Facial recognition is used for two purposes: identification and categorization. Identification works by matching live or recorded digital footage to pre-existing images on a database. When such information is "categorised", the effects of facial recognition become more wide-reaching. The technology can automatically filter people into categories according to identifiable features such as age, gender, weight or even presumed sexual orientation without the help of a real person.

The algorithm organises people into categories by comparing images to the "statistical representation of the average of that category." If available on a wide scale, facial recognition technology raises therefore the possibility of biometric mass surveillance. That's because CCTV and other cameras, ubiquitous both in public and private spaces, can be updated with facial recognition capacities. Facial recognition technology is already deployed by law enforcement and private actors in a wide range of places, including schools and train stations.

Worryingly, more often than not our images are uploaded to facial recognition databases without our awareness or consent. For example, it has recently been discovered that Clearview AI, a US technology firm, scraped over three billion facial images from social media platforms such as Facebook, YouTube and Twitter, and gave access to our data to governments and private firms in exchange for payments.

Such incredibly private information is currently being used by law enforcement to make policing decisions, and might in the future be employed by public or private bodies to make decisions as to e.g. your suitability for a job, a university course or loans, overseas travel, or entry into a festival.

How much does facial recognition affect everyday life?

Whilst facial recognition technology may seem akin to science fiction, it is silently creeping its way into the European policy mainstream. After trialling facial recognition at Berlin-Südkreuz station since 2018, Germany is currently planning to roll out biometric surveillance systems in an additional 134 train stations and 14 airports despite criticism. France relies heavily on facial recognition technologies, with President Macron set to catch up with China and the U.S.

Italian police also started deploying the Automatic Image Recognition System (SARI) in 2017, targeting not just a few individuals but processing the biometric data of everyone present in spaces monitored for the purpose of identifying certain individuals. Recently, Belgrade (Serbia) installed hundreds of face recognition cameras, creating a permanent surveillance system. Unbeknownst to much of the general public, facial recognition is becoming increasingly common throughout Europe. Luckily, recent legal rulings in some countries have found such facial recognition systems to be unlawful.

What are the Pros?

Face recognition is promoted as something that makes our lives more convenient. Instead of having to bother entering a password into our phone, for example, or needing to show our ID at an airport, our faces will be enough to verify who we are. In Osaka at four train stations facial recognition systems were implemented to let people pass just by scanning their faces, without using a ticket or an ID card. A transport official noted that they would "emphasize as an advantage the fact that passengers with large luggage will be able to pass gates simply by showing their faces instead of looking for tickets".

While the internal processes for biometric authentication are technical, from a user's point of view it's incredibly easy and quick. Placing a finger on a scanner and unlocking an account in seconds is faster than typing out a long password that has multiple special characters. In addition, forgetting a password is a common mistake of most users. The chances of you forgetting your own biometrics? 0%!

Another argument is that facial recognition helps ensure security. Biometrics are said to provide increased levels of assurance that a person trying to access a service or make a transaction is real. Those in favour of using biometrics point out that passwords, PINs and other personal identifying information can be compromised by data breaches, allowing fraudsters to access accounts that use traditional authentication methods. In contrast, it's more difficult for someone other than you to provide your fingerprint on the spot. When it comes to law enforcement, those favouring facial recognition argue that it allows the police to track down suspects more easily.

What about the Cons?

Facial recognition fails to respect our rights in more than one way. First, the technology intrusively acquires some of our most intimate data. Because our data belongs to us, (e.g. under the General Data Protection Regulation), we have the right to know when personal information is obtained about us and for which purpose.

Second, knowing that we can be easily identified can lead us to self-censor for fear of negative consequences. For example, people who know that they will be identified and placed in a database for showing up at a protest may be put off attending. This is especially damaging in situations where governments have illegally restricted the right to demonstrate to stifle public criticism.

Third, biometric data carries its own security risk. Unlike a password, biometric data cannot be changed. If my fingerprint data leaks, this is not something I can "reset" like a password. To make things worse, your face can be scanned anytime and anywhere, without your consent. This means that your biometric data might actually be stored in a range of databases, whose security measures might be inadequate.

Facial recognition technology is also known to work relatively well on white and male faces, while having a high rate of inaccuracy on people of colour, especially if they are female. This means that people from ethnic minority groups, who already tend to have less easy access to services and amenities, will encounter an extra barrier when authorities use facial recognition in these contexts.

Not only does face recognition discriminate against people of colour, but it is also disproportionately targeted at marginalized individuals. In a law enforcement context, facial recognition is predominantly used in communities that are already over-policed. In Italy, for instance, the police's facial recognition software database contained 2 million images of Italians, compared to 7 million of refugees and migrants. So the way this technology is used is likely to perpetuate the way people from some groups are already disproportionately targeted.

Conclusion

Facial recognition might seem attractive for its potential to simplify our lives and enhance public security. But it's more likely to end up as a major inconvenience that makes it easy to remove our freedoms and choices and reinforce inequality and discrimination. It's becoming more popular with governments, so the law has to catch up and stop this technology being used at all in most contexts.

Liberties and facial recognition

We at Liberties seek to ban mass surveillance through facial recognition. A few months ago, we joined the ReclaimYourFace movement. If you agree that facial recognition has no place in our public spaces, sign the petition here.

# data[].attributes.attachment_link.description
Facial recognition technology is a hot topic, currently attracting heated debates worldwide. On the one hand, facial recognition technology is seen as highly efficient. It is fast and (argued to be) objective in performing tasks.

# data[].attributes.headline
How does ethical facial recognition work?

# data[].attributes.subject
How does ethical facial recognition work?

# data[].attributes.body
Main idea:

Facial recognition is essentially being able to recognise a human face through technology.
Since the recent rise in the popularity of human recognition in the past few years, there have been many ethical controversies behind it. While facial recognition can help verify someone's identity, it also raises privacy issues.
How does it work?
Recognising faces might sound like quite an easy task. You're familiar with a family, friend or acquaintance's face. That's how facial recognition works, but on a much greater scale:
1. An image of your face is taken.
2. Your face's geometry is identified.
3. Your facial signature is compared to other known faces.
4. A determination is made.

# data[].attributes.attachment_link.title
What is facial recognition? How facial recognition works

# data[].attributes.attachment_link.description
Facial recognition is a way of recognizing a human face through technology. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match.

# data[].attributes.headline
Philosophers Lead Academics’ Effort To Restrict Facial Recognition Technology

# data[].attributes.subject
Philosophers Lead Academics’ Effort To Restrict Facial Recognition Technology

# data[].attributes.body
Main idea:
Facial recognition is targeted disproportionately.

Arguments/facts:
- mainly picking refugees and migrants.

Dialog:

- why are police on Italy so unfear?
- why do they have a record in refugees and immigrants and not in Italians?

Summary:

Facial recognition discriminates many people not only because of their color but also because of how they live in general and the situations in which they live. An example is Italy since the police of this country contained 2 million images of Italians, and of refugees and immigrants 7 million just where they come from. Therefore, it is likely that this technology is blown out of proportion.

APA:
Nikolett, A. & Alice, N. (August 03, 2021).

Facial Recognition: Pros And Cons. Retrieved on https://www.liberties.eu/en/stories/pros-and-cons-of-facial-recognition/43708

# data[].attributes.attachment_link.readable_text
Facial recognition technology is a hot topic, currently attracting heated debates worldwide. On the one hand, facial recognition technology is seen as highly efficient. It is fast and (argued to be) objective in performing tasks. On the other hand, there is a concern that the biases and inaccuracies inherent in these systems might lead to discrimination and jeopardise fundamental human rights, such as our freedom to protest against measures we disagree with.

What is facial recognition and how does it work?

The meaning of facial recognition is perhaps intuitive, but it won't harm to delve into some detail about what the technology really entails. The UK's Information Commissioner's Office defines facial recognition as "the process by which a person can be identified or otherwise recognized from a digital image." Just like an ID card, your face can serve as a proof of identity.

Facial recognition is used for two purposes: identification and categorization. Identification works by matching live or recorded digital footage to pre-existing images on a database. When such information is "categorised", the effects of facial recognition become more wide-reaching. The technology can automatically filter people into categories according to identifiable features such as age, gender, weight or even presumed sexual orientation without the help of a real person.

The algorithm organises people into categories by comparing images to the "statistical representation of the average of that category." If available on a wide scale, facial recognition technology raises therefore the possibility of biometric mass surveillance. That's because CCTV and other cameras, ubiquitous both in public and private spaces, can be updated with facial recognition capacities. Facial recognition technology is already deployed by law enforcement and private actors in a wide range of places, including schools and train stations.

Worryingly, more often than not our images are uploaded to facial recognition databases without our awareness or consent. For example, it has recently been discovered that Clearview AI, a US technology firm, scraped over three billion facial images from social media platforms such as Facebook, YouTube and Twitter, and gave access to our data to governments and private firms in exchange for payments.

Such incredibly private information is currently being used by law enforcement to make policing decisions, and might in the future be employed by public or private bodies to make decisions as to e.g. your suitability for a job, a university course or loans, overseas travel, or entry into a festival.

How much does facial recognition affect everyday life?

Whilst facial recognition technology may seem akin to science fiction, it is silently creeping its way into the European policy mainstream. After trialling facial recognition at Berlin-Südkreuz station since 2018, Germany is currently planning to roll out biometric surveillance systems in an additional 134 train stations and 14 airports despite criticism. France relies heavily on facial recognition technologies, with President Macron set to catch up with China and the U.S.

Italian police also started deploying the Automatic Image Recognition System (SARI) in 2017, targeting not just a few individuals but processing the biometric data of everyone present in spaces monitored for the purpose of identifying certain individuals. Recently, Belgrade (Serbia) installed hundreds of face recognition cameras, creating a permanent surveillance system. Unbeknownst to much of the general public, facial recognition is becoming increasingly common throughout Europe. Luckily, recent legal rulings in some countries have found such facial recognition systems to be unlawful.

What are the Pros?

Face recognition is promoted as something that makes our lives more convenient. Instead of having to bother entering a password into our phone, for example, or needing to show our ID at an airport, our faces will be enough to verify who we are. In Osaka at four train stations facial recognition systems were implemented to let people pass just by scanning their faces, without using a ticket or an ID card. A transport official noted that they would "emphasize as an advantage the fact that passengers with large luggage will be able to pass gates simply by showing their faces instead of looking for tickets".

While the internal processes for biometric authentication are technical, from a user's point of view it's incredibly easy and quick. Placing a finger on a scanner and unlocking an account in seconds is faster than typing out a long password that has multiple special characters. In addition, forgetting a password is a common mistake of most users. The chances of you forgetting your own biometrics? 0%!

Another argument is that facial recognition helps ensure security. Biometrics are said to provide increased levels of assurance that a person trying to access a service or make a transaction is real. Those in favour of using biometrics point out that passwords, PINs and other personal identifying information can be compromised by data breaches, allowing fraudsters to access accounts that use traditional authentication methods. In contrast, it's more difficult for someone other than you to provide your fingerprint on the spot. When it comes to law enforcement, those favouring facial recognition argue that it allows the police to track down suspects more easily.

What about the Cons?

Facial recognition fails to respect our rights in more than one way. First, the technology intrusively acquires some of our most intimate data. Because our data belongs to us, (e.g. under the General Data Protection Regulation), we have the right to know when personal information is obtained about us and for which purpose.

Second, knowing that we can be easily identified can lead us to self-censor for fear of negative consequences. For example, people who know that they will be identified and placed in a database for showing up at a protest may be put off attending. This is especially damaging in situations where governments have illegally restricted the right to demonstrate to stifle public criticism.

Third, biometric data carries its own security risk. Unlike a password, biometric data cannot be changed. If my fingerprint data leaks, this is not something I can "reset" like a password. To make things worse, your face can be scanned anytime and anywhere, without your consent. This means that your biometric data might actually be stored in a range of databases, whose security measures might be inadequate.

Facial recognition technology is also known to work relatively well on white and male faces, while having a high rate of inaccuracy on people of colour, especially if they are female. This means that people from ethnic minority groups, who already tend to have less easy access to services and amenities, will encounter an extra barrier when authorities use facial recognition in these contexts.

Not only does face recognition discriminate against people of colour, but it is also disproportionately targeted at marginalized individuals. In a law enforcement context, facial recognition is predominantly used in communities that are already over-policed. In Italy, for instance, the police's facial recognition software database contained 2 million images of Italians, compared to 7 million of refugees and migrants. So the way this technology is used is likely to perpetuate the way people from some groups are already disproportionately targeted.

Conclusion

Facial recognition might seem attractive for its potential to simplify our lives and enhance public security. But it's more likely to end up as a major inconvenience that makes it easy to remove our freedoms and choices and reinforce inequality and discrimination. It's becoming more popular with governments, so the law has to catch up and stop this technology being used at all in most contexts.

Liberties and facial recognition

We at Liberties seek to ban mass surveillance through facial recognition. A few months ago, we joined the ReclaimYourFace movement. If you agree that facial recognition has no place in our public spaces, sign the petition here.

# data[].attributes.attachment_link.description
Facial recognition technology is a hot topic, currently attracting heated debates worldwide. On the one hand, facial recognition technology is seen as highly efficient. It is fast and (argued to be) objective in performing tasks.