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Facial recognition can be simply described as a technology capable of identifying or confirming an individual’s identity using their face. Generally, this identification is used to access an application, system, or service.

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The pioneers of AI and Facial Recognition.

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Who was the first person to develop this technology?
The earliest pioneers of facial recognition were Woody Bledsoe, Helen Chan Wolf, and Charles Bisson. From 1964 to 1966, Bledsoe, along with Wolf and Bisson began doing research on using computers to recognize the human face, but the experiments weren't successful. In the 1970s, Goldstein and Harmon used 21 specific subjective markers such as hair color and lip thickness to automate the recognition. In 1988, Kirby and Sirovich applied the standard linear algebra technique to the problem of facial recognition. After only 3 years, in 1991 Turk and Pentland discovered how to detect faces within an image, which paved the way for future developments in Facial Recognition technology. Since then, facial recognition systems have seen wider uses in recent times on smartphones and in other forms of technology, such as robotics.

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A face authentication system based on principal component analysis and neural networks - which is based on the idea of the human brain including a huge number of neurons, mimicking the way these biological neurons signal to one another. Input data will go through many stages (layer), being analyzed and categorized by deep learning algorithms and finally, it becomes the result we want, in this situation is the identification of someone.

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To be more specific, we divide the process of Facial Recognition Operation into 4 stages as follows:

Step 1: Face detection

Step 2: Face analysis

Step 3: Converting the image to data

Step 4: Recognition

Step 1:  Face detection
The camera detects and locates the image of a face, either alone or in a crowd. The image may show the person looking straight ahead or in profile.
Step 2: Face analysis
An image of the face is captured and analyzed. Most facial recognition technology relies on 2D rather than 3D images because it can more conveniently match a 2D image with public photos or those in a database. The software reads the geometry of your face and identifies the facial landmarks that are key to distinguishing. For instance, the distance between your eyes, the depth of your eye sockets, the distance from forehead to chin, the shape of your cheekbones,...etc.
Step 3: Converting the image to data
The face capture process transforms analog information (a face) into a set of digital information (data) based on the person's facial features. Your face's analysis is essentially turned into a mathematical formula. The numerical code is called a faceprint. In the same way that thumbprints are unique, each person has their own faceprint.

Step 4: Recognition
Your faceprint is then compared against a database of other known faces. For example, the FBI has access to up to 650 million photos, drawn from various state databases. On Facebook, any photo tagged with a person’s name becomes a part of Facebook's database, which may also be used for facial recognition. If your faceprint matches an image in a facial recognition database, then a determination is made.

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Face recognition technology is used in many cases:
✔ Second authentication factor
✔ Access to mobile application
✔ Law enforcement
✔ Finding missing persons
✔ Tracking student or worker attendance
✔ Payment method

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1. Apple uses facial recognition to help users quickly unlock their phones, log in to apps, and make purchases.

2. Google incorporates the technology into Google Photos and uses it to sort pictures and automatically tag them based on the people recognized.

3. McDonald’s has used facial recognition in its Japanese restaurants to assess the quality of customer service provided there, including analyzing whether its employees are smiling while assisting customers.

4. Snapchat is one of the pioneers of facial recognition software: it allows brands and organizations to create filters which mould to the user’s face — hence the ubiquitous puppy dog faces and flower crown filters seen on social media.

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Increased security:
On a governmental level, facial recognition can help to identify terrorists or other criminals. On a personal level, facial recognition can be used as a security tool for locking personal devices and for personal surveillance cameras.

Reduced crime:
Face recognition makes it easier to track down criminals. Aside from physical security, there are benefits to cybersecurity as well. For example,  companies can use face recognition technology as a substitute for passwords to access computers.

Faster processing:
With the help of FR, the process of recognizing a face takes only a few seconds, which enables quick and efficient verification of a person’s identity.

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Massive data storage:
Facial recognition software relies on machine learning technology, which requires massive data sets to “learn” to deliver accurate results.

Scope for error:
Facial recognition data is not free from error, which could lead to people being implicated for crimes they have not committed. For example, a slight change in camera angle or a change in appearance, such as a new hairstyle, changes of expression, plastic surgery, etc could lead to error. In 2018, Newsweek reported that Amazon’s facial recognition technology had falsely identified 28 members of the US Congress as people arrested for crimes.

Privacy Concerns:
Some worry that the use of facial recognition along with ubiquitous video cameras( such as CCTV cameras), AI, and data analytics creates the potential for mass surveillance, which could restrict individual freedom and pose a threat to our privacy.

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1. Technology Capable

2. Access

3. Application

4. System

5. Computer

6. Smartphone

7. Robotic

8. Software

9. Machine Learning

10. Cybersecurity

11. Deep learning

12. Algorithm

13. Artificial neural network

14. Authentication

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https://www.kaspersky.com/resource-center/definitions/what-is-facial-recognition

https://en.wikipedia.org/wiki/Facial_recognition_system

https://krazytech.com/technical-papers/face-identification-recognition/amp?fbclid=IwAR0wkAxQpYhVTZKBqkiV_neBvPjsfFbZ9l4hLtp3VE6d5h1Mh8Q9fMs4LP0

https://www.electronicid.eu/en/blog/post/face-recognition/en?fbclid=IwAR2Hmqd7L5AilmXfhlhk48sDWifisLF9hSgn-3_TaWljSgVv_MbhDAkAX9U