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Funny faces

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Choose one topic and talk about it for one minute with your group. Try and keep talking for the 60 seconds!

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Transnational Crime Presentation - Group photo

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Transnational Crime Presentation - Group photo

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Today's Powerpoint with ANSWERS from Listening

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Today's Powerpoint with ANSWERS from Listening

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Start studying Refugee Dilemma - Vocabulary. Learn vocabulary, terms, and more with flashcards, games, and other study tools.

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What flags do I have a picture of?

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The Institute of Continuing & TESOL Education

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Homework - for 6th May            Test yourself - how good are you?  I got 69% correct!

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Homework - for 6th May            Test yourself - how good are you?  I got 69% correct!

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You have to do this on a computer, not on a mobile phone. (Time: 10 minutes)

It is being used for research purposes so for ethical reasons, you need to give consent.

It is anonymous if you wish.

1. Click "Start Test"

2. Tick the "Consent form" box

3. Click "I agree"

4. Read the instructions carefully

5. Complete the face-match test

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We have designed a very challenging web-based test of face identification ability to try and identify individuals with superior face recognition abilities. Our test is difficult because it requires people to recognise faces despite substantial changes in appearance from one encounter to the next, caused for example by differences in age, pose, lighting, and expression.

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Screen 1

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Australia's Refugee Policy: An Overview (by UNSW)

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Australia's Refugee Policy: An Overview (by UNSW)

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Australia’s key refugee policies briefly explained

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A. Open just the file for the question (1-9) you have been allocated.
If you wish, you can get English subtitles by pressing the CC button (bottom right corner)

B. Scrolling the time bar to the right (bottom of video), watch your section (1-9) from the time in brackets (m=minutes; s=seconds) to the beginning of the next question.

C.Write notes about how each of the participants responded to the question/s you’ve been allocated.

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Jigsaw Listening - You Can't Ask That - Refugees

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Participants photo + Names - Reference for Jigsaw Listening

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Participants photo + Names - Reference for Jigsaw Listening

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ABC iView - "You Can't Ask That" - Refugees (S2 Ep7) - Jigsaw Listening Activity

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ABC iView - "You Can't Ask That" - Refugees (S2 Ep7)

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ABC iView - "You Can't Ask That" - Refugees (S2 Ep7)

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Diverse Australians living in judgement confront prejudice and discrimination head-on. The show asks the hard questions and turns the answers into surprising moments of honesty and heart-warming humour. 2019 AACTA Award Winner for Best Factual Entertainment.

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Diverse Australians living in judgement confront prejudice and discrimination head-on. The show asks the hard questions and turns the answers into surprising moments of honesty and heart-warming humour. 2019 AACTA Award Winner for Best Factual Entertainment.

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Background to the refugee crisis in Australia

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Background to the refugee crisis in Australia

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Listening Task p.44

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The Newsmakers - Australia’s Refugee Crisis

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Listening Task p.45

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Professor Jane McAdam - The Refugee Convention

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Reading Task p.46

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Screen 2

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Newsela - News articles you can adjust the language to suit your level.

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Newsela - News articles you can adjust the language to suit your level.

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Reading Resource - anytime you want to read an English news article at a level to suit your level. Top right tab:
"Max" = Maximum level - Advanced; 1260L = Upper Intermediate
880L = intermediate
560L = Pre-Intermediate;
330L = Elementary

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Australia has a national asylum system in place to determine whether people claiming asylum are refugees.

UNHCR closely monitors the situation in Australia and works to promote respect for the international human rights of asylum-seekers.

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United Nations High Commissioner for Refugees

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Australia has a national asylum system in place to determine whether people claiming asylum are refugees. UNHCR closely monitors the situation in Australia and works to promote respect for the international human rights of asylum-seekers.

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Detention Statistics

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Key numbers

The Department of Home Affairs (formerly the Department of Immigration and Border Protection) publishes on its website monthly and yearly statistics about people it detains. Its monthly statistics include the number of people in detention facilities in Australia, offshore processing centres, and in the community (either under a 'residence determination', or with a 'bridging visa'.)

You can download the graphs and the data here. The embedded version of the spreadsheet has been removed due to changes in compatibility with mobile devices.

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Detention statistics for Australia - Refugee Council of Australia

# data[].attributes.attachment_link.description
The Department of Home Affairs (formerly the Department of Immigration and Border Protection) publishes on its website monthly and yearly statistics about people it detains. Its monthly statistics include the number of people in detention facilities in Australia, offshore processing centres, and in the community (either under a 'residence determination', or with a 'bridging visa'.)

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Dept of Home Affairs - Refugee and Humanitarium Program

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Dept of Home Affairs - Refugee and Humanitarium Program

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Australia's refugee and humanitarian program is an important part of our contribution to the international protection of refugees.

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Homework - 5th May            Test yourself - how good are you?  I got 69% correct!

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Homework - 5th May            Test yourself - how good are you?  I got 69% correct!

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You have to do this on a computer, not on a mobile phone. (Time: 10 minutes)

It is being used for research purposes so for ethical reasons, you need to give consent.

It is anonymous if you wish.

1. Click "Start Test"

2. Tick the "Consent form" box

3. Click "I agree"

4. Read the instructions carefully

5. Complete the face-match test

# data[].attributes.attachment_link.description
We have designed a very challenging web-based test of face identification ability to try and identify individuals with superior face recognition abilities. Our test is difficult because it requires people to recognise faces despite substantial changes in appearance from one encounter to the next, caused for example by differences in age, pose, lighting, and expression.

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Catalyst - Passport Problem (Transcript)

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Catalyst - Passport Problem (Transcript)

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Each member of your group reads a different article.

1.Read your article to get the main idea.

2. Tell your group about the article you read.

3. Decide together what the articles are suggesting about surveillance and facial recognition.

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Surveillance & Recognition jigsaw reading

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Video for in class

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Problem-solution Presentation: Q & A  with annotations

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subscribe to Short Wave podcast

People who struggle to remember faces can blame their parents.

That's because the ability to remember a face is inherited, according to a paper in the Proceedings of the National Academy of Sciences. Researchers also found that people who are good at remembering faces are not necessarily good at other memory tasks.

Taken together, these results strongly support the idea that face recognition ability comes from a dedicated circuit, or set of circuits, in the brain.
Identical Twins, Nearly Identical Ability To Remember
The paper, which comes from an international team, included two different studies.

The first looked at nearly 300 pairs of twins. Some were identical, meaning they share all their genes; others were nonidentical twins, who share only half their genes.

All the twins took a test that involved memorizing six faces. Then, participants were shown three more faces and were asked to identify the one face in this group they had seen before.

The face recognition ability of pairs of nonidentical twins often differed quite a bit, says Jeremy Wilmer, the study's lead author and a psychologist at Wellesley College in Massachusetts.

But that wasn't the case with pairs of identical twins, whose performance was "extraordinarily similar," Wilmer says.

These results offer strong evidence that "face recognition ability is a highly familial trait," Wilmer says.
Using A Special Part Of The Brain
The second study used a Web site to test the ability of several thousand people to remember faces, word pairs and abstract art images. The study found no link between face recognition ability and scores on the other tasks.

All of this supports the idea that there is a part of the brain that specializes in processing faces, says Nancy Kanwisher, a brain scientist at MIT.

The most likely candidate is the "fusiform face area," which is located "just behind and underneath, and a bit from your right ear," Kanwisher says.

She says it makes sense that our genes would include special code for such an area, because faces are so important to humans and some other primates.

Faces tell us whether we know a person, what mood they're in, how old they are, and whether they are looking at us or something else, Kanwisher says. "All this rich visual information you can get from a brief glimpse of a face."

Face recognition appears to be so basic, she says, that it is actually hard-wired into the brain.
Similar Findings In Monkeys
Japanese researchers provided strong evidence of this in an experiment with monkeys a couple of years ago, Kanwisher says.

The researchers spent years raising monkeys who never saw a face - human or monkey. The animals were separated from other monkeys, and their human caretakers wore masks.

Even so, when the monkeys were tested, they had "adult-like face discrimination abilities," Kanwisher says, adding that this is probably because the ability to recognize faces is carried in our genes and present from birth.

The finding in monkeys is consistent with experiments on human babies just a few days old, Wilmer says.

When a baby is presented with representations of things like a circle or square and a face, Wilmer says, "The baby will spend a lot of time looking at the face relative to the other things."

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subscribe to Short Wave podcast People who struggle to remember faces can blame their parents. That's because the ability to remember a face is inherited, according to a paper in the Proceedings of the National Academy of Sciences. Researchers also found that people who are good at remembering faces are not necessarily good at other memory tasks.

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A Survey on Face Recognition of Identical Twins

Kanchan Patil, Prof. Sachin Bojewar

Abstract - Recent studies have shown that face recognition performance degrades considerably for images of identical twins. Human face matching capability is often taken into consideration as a bench-mark for assessing and improving automatic face recognition algorithms. Here, this paper will show human capability to distinguish between identical twins. If humans are able to distinguish between facial images of identical twins, it would suggest that humans are capable of identifying discriminating facial traits that can potentially be useful to develop algorithms for this very challenging problem. If humans viewing a pair of facial images can perceive if the image pairs belong to the same person or to a pair of identical twins. The paper consists of experiments results, which are conducted on 186 twin subjects, making it the largest such study in the literature to date. And observation will show that humans can perform the task significantly better if they are given enough time and tend to make more mistakes when images differ in imaging conditions. The paper analysis also suggests that humans look for facial marks like moles, scars, etc. to make their decision and do worse when presented with images lacking such marks. Experiments with automatic face recognition systems show that human observers outperform automatic matchers for this task

Index Terms- siblings, facial images, Gabor wavelets, biometrics, iris, identical twins, HSIRB

----------  ----------
Recognition of facial images of identical twin siblings pos- es a considerable challenge for any face recognition algorithm because of the strong similarity between the face images. Re- cent research has showed that the performance of automatic face recognition technology deteriorates drastically when the images belong to identical twin siblings as compared to when they correspond to unrelated persons [1]. The degradation is shown to be far more drastic for face than for other biometrics such as iris and fingerprint. Humans are very good at identify- ing people from their images, and so human face recognition performance is often considered as a guideline for assessing face recognition algorithms [2]. To the best of our knowledge, no systematic human study has been performed that address- es the task of distinguishing between identical twins from their face images. Here, we perform experiments to determine if humans viewing a pair of facial images can perceive wheth- er the images belong to the same person or to a pair of identi- cal twin siblings. If humans are able to distinguish between facial images of identical twin siblings, it might mean that they are capable of observing discriminating traits which can po- tentially be used to improve the performance of face recogni- tion technology. In this investigation, human participants view pairs of facial images and respond according to their level of certainty whether they belong to the same person or to identical twins. First, we study the human performance when

----------------

* Kanchan Patil is currently pursuing master's degree program in Computer engineering in Mumbai University, India.
E-mail: kanchan.patil218@gmail.com
* Prof. Sachin Bojewar, Asst. Prof., Dept. of Computer Engineering, Vidya- lankar Institute of Technology, Mumbai University, India.

E-mail: sachin.bojewar@vit.edu.in
the participants view the images for a limited time of two se- conds, which has been shown to be sufficient for matching
images of unrelated persons [2]. We conduct another experi- ment to analyse whether humans can do better when the viewing time is increased. The variation in performance when the input images are taken under controlled indoor conditions or in an outdoor environment is also analysed as part of the second experiment. We also study which facial features are most useful for humans to correctly distinguish between iden- tical twins. The human performance is also compared against traditional and commercial automatic face matchers. The re- sults of this investigation can be used to improve the perfor- mance of existing face recognition algorithms so that they are more suited to handle the challenges posed by facial images of identical twin siblings.

Since identical twins cannot be distinguish by their DNA, there is increased interest in using different biometrics traits for distinguishing between identical twins. Modelling facial expressions as isometric of the facial surface. A hybrid feature by combining the traditional holistic facial appearance feature with a facial dynamics feature has also been shown to be suc- cessful in distinguishing between facial images of one pair of identical twins [3]. A face recognition system based on an op- tical recognition principle was also shown to be successful in distinguishing between identical twin siblings for a database of ten pair subjects [4]. Soft biometric facial marks have been used to differentiate identical twins on a dataset which con- tained facial images from five pairs of identical twins [5]. Since they were tested on very small number of twin pairs, the con- clusion may not be statistically significant. Sun et al. [1] con- ducted unimodal and multimodal matching experiments on fingerprint, face and iris biometrics collected from 66 pairs of identical twins. They focused on issue that it is much easier to

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distinguish between identical twin siblings using iris and fin- gerprint biometric compared to using facial images. There has been some work on distinguishing between identical twins based on other biometrics like palm, fingerprint, iris [6], and speaker identification [8].
Humans are naturally trained to recognize faces from birth
and there is strong evidence that suggests face recognition
activity in humans takes place in the fusiform face area of the cortex. Thus there has been a lot of interest in developing al- gorithms which replicate the human visual processing for face recognition. For example, biologically inspired features in the form of Gabor wavelets have been successfully used for rec- ognizing faces. It has been also seen that the performance of automatic algorithms can be considerably improved by fusing it with human performance. Though quite a few human stud- ies have been conducted in the past to study various aspects of general face matching problem [2], to the best of our knowledge, there has been no systematic study of human abil- ity to distinguish between identical twins from their face im- ages.

In this section, we will discuss the dataset used and the re- sults obtained through that experimental setup, and these set- up explained as, the participants who took part in the study and the experiment protocol.

3.1 Dataset:

The twins data used in these setup was obtained from data collection sessions at the Twins Days Festival in
Twinsburg, Ohio in August 2009 [10]. The dataset consists of
186 subjects, of which 34 are male and the remaining 152 are
female. The twins participating in the data collection report their identity as identical twins. No DNA testing was per- formed to confirm the claims. All data collected at the festival followed a data collection protocol approved by the Human Subjects Institutional Review Board (HSIRB) at the University of Notre Dame.

3.2 Participants:

A total of 23 volunteers were appointed to participate in the recognition experiment. They did not undergo any prior prac- tice or training for the work. The volunteers were offered ten dollars for participation, and an additional five dollars if they correctly classified 80% or more of the twin image pairs. The experiment was approved by HSIRB at the University of Notre Dame.

3.3 Experiment Protocol:

In these experiment, the participants were given a brief verbal description of the study, and asked to read and sign a form. Then they were asked to start a computer program that pre- sented instructions along with a few sample trials. This was followed by 180 trials out of which 90 trials corresponded to match pairs while the other 90 corresponded to non-match
pairs. The match and non-match pairs were separated and presented in a random order to each participant. The images used in this experiment were captured in an indoor environ- ment with controlled lighting, frontal pose and neutral expres- sion. The images were cropped based on the eye locations so that only the face portion was visible. This confirmed that the responses of the participants were not affected by external factors like clothing, hair style, etc. In each attempt, the com- puter program shown a pair of facial images followed by a prompt for a decision on whether they correspond to the same person or to identical twin siblings. The images were shown for two seconds. Then the participants were asked the follow- ing question:
Are the two images of the same person or of identical twin
siblings?
They were required to select one out of five possible respons-
es.
1) Sure they are the same person
2) Think they are the same person
3) Don't know
4) Think they are identical twin siblings

5) Sure they are identical twin siblings

Fig. 1. An example of pair of displayed images. Here the images are of the same person

Fig. 2. An example of a pair of displayed images. Here the images are of identical twin siblings.

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Fig. 1 and Fig. 2 show examples of face images shown. The two images in Fig. 1 belong to the same person whereas the two images in Fig. 2 belong to identical twin siblings. Fig. 1. An example of a pair of shown images. Here the images are of the same person.

In this section, we describe in detail the results obtained
from the experiment described above.

4.1 Are humans able to distinguish between identical

twins?

To find the overall accuracy, we count the number of times each participant correctly classified the pair of images to be of the same person or of identical twin siblings. For example, if the two images are of the same person, we consider the re- sponses Sure they are the same person and Think they are the same person as correct responses. Similarly, if the two images are of identical twin siblings, we consider the responses Sure they are identical twin siblings and Think they are identical twin siblings as correct responses. Across the 23 participants, the maximum accuracy attained is 90.56% and the minimum accuracy is 60.56%. The average accuracy is 78.82%. We use a one-tailed t-test to evaluate the null hypothesis that humans did not perform better on this work than random guessing. Thus, this produced statistically significant evidence that the participants performed better than random. The Receiver Op- erating Characteristic (ROC) of the performance is shown in Fig. 3 (blue dotted curve). The ROC provides a complete pic- ture of human accuracy in distinguishing between identical twins at the assessed confidence threshold levels (1 through 5) and is drawn using the same procedure as in [2]. The verifica- tion rate or hit rate is computed as the proportion of matched pairs correctly judged to be of the same person. The false ac- ceptance rate or false alarm rate is calculated as the proportion of non-match pairs judged incorrectly to be of the same per- son.
Fig. 3. Human performance when the image pairs are viewed for limited time vs. unlimited time

4.2 Do humans perform better when they are certain?

As mentioned previously, the participants had the choice to respond according to their level of confidence. For example, if the participant felt that the two images belonged to the same person, they could choose either Sure they are the same person or Think they are the same person, depending on their confidence level. It shows that the confidence level varies significantly across the participants. On one hand, one of the participants was certain for 118 out of 180 trials, while on the other hand, three participants were not certain of any of their responses. The average number of certain responses across all participants was 60 out of 180 trials. Considering the trials for which the participants were certain about their response, the average accuracy of correct classification of the image pairs as belonging to the same person or to a pair of identical twins is
93.12%. Thus the performance is significantly better on the
subset of trials where the participants were certain about their response.

4.3 Self-learning

The participants who volunteered for the study did not undergo any prior training to classify images of identical twins and none of them had an identical twin sibling. Here it is analyze whether the participants can learn by themselves the subtle differences between the facial images of identical twins. The average improvement in the performance in the second half of the try as compared to the first half is 1.5%. Out of the 23 participants, 14 performed better in the second half, while only seven performed better in the first half. This improvement might mean that as the participants viewed more images of twins, they trained themselves and performed better in the second half of the trials. A one-tailed t-test shows that the difference is not statistically significant (p-value

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0.3065). In this study they mentioned, the participants did not receive any feedback after each visual stimuli whether their response was correct or not. Providing feedback on their response could have helped the participants learn better and thus perform better in the second half as compared to the first half.

4.4 Are males more easy to classify than females?

Several researchers have studied the effect of gender on the face recognition performance, and though individual studies find men or women easier to recognize, there is no consistent gender effect [10]. Here, researcher investigated if such gender effects are present when humans are asked to distinguish between identical twins. The number of male pairs in the twins data used for the experiment is considerably lower than that of female pairs. The total number of correct and incorrect responses for male pairs is 571 and 153 respectively with an accuracy of 78.87%. On the other side, the total number of correct and incorrect responses for female pairs is 2693 and
723 respectively with an accuracy of 78.84%. So there is no
significant difference in matching accuracy for male and
female pairs. Similar results have been reported for facial images of unrelated persons [10].

As mentioned in above analysis that humans do not perform very well in distinguishing between images of identical twin siblings given just two seconds to view images. Studies have shown that for face images of unrelated persons taken under different illumination conditions, increasing the viewing time of the images beyond two seconds does not significantly increase the recognition performance of human participants [2]. So here author designrd another experiment to test if humans can do a better job in distinguishing between identical twins when given sufficient time. He also want to test how external imaging factors affect the human performance and understand which facial features are most important for humans to distinguish between identical twin siblings. The visual stimuli used for this experiment consisted of 100 pairs out of which 50 pairs were images captured under controlled indoor conditions while the remaining 50 pairs were captured in outdoor uncontrolled environment. For each subset of 50, 25 were match pairs (images of same person) and the other 25 were non-match pairs. The four subsets of image pairs were interspersed and presented in a random order to the participants and the order was different for each participant. In this experiment, the participants were given unlimited time to view the image pairs and make their decision [7]. As in the first experiment, each trial consisted of the computer program showing a pair of facial images with a prompt for a decision on whether they belong to the same
person or identical twin siblings. The participants were also asked which features helped them make the decision. They were given the following choices: Eyebrows, Eyes, Nose, Lips, Moles/Scars/Freckles, Skin color/Texture, Wrinkles, Facial hair or Make-up.

5.1 Do humans perform better when given unlimited time?

One of the goals of mentioned experiment is to explore if increasing the viewing time makes it easier for humans to distinguish between identical twins. Across the 25 participants, the maximum accuracy attained is 100% and the minimum accuracy is 78%. The mean recognition accuracy for the mentioned experiment is 92.88% and the median is 95%. Fig. 3 (red solid curve) shows the ROC obtained. For generating the ROC, author considered only the indoor image trials from the second experiment for fair comparison. As observed that increasing the viewing time significantly increases the matching accuracy. The significant improvement in performance with increase in viewing time that is observeed in executed experiment can be attributed to the fact that facial images of twins are very similar with only subtle differences which can be better perceived given sufficient time [7]. Authors interpretation is that the added time is used by the participants to consider local features in making their decision.

5.2 Do humans perform better on controlled image

pairs than on uncontrolled pairs?

Fig. 4 shows a pair of uncontrolled images used in the experiment. Although the images are of the same person, they
appear very different due to illumination effects.

Fig. 4. Example of a pair of images of the same person taken outdoors with uncontrolled illumination

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Fig. 5. Human performance for image pairs taken in controlled and uncontrolled settings.
As described earlier, out of the 100 image pairs used in the second experiment, 50 were controlled image pairs and the remaining 50 were uncontrolled image pairs with equal number of match and non-match pairs of each type. The mean accuracy obtained here for the controlled image pairs is
94.96% and that for the uncontrolled image pairs is 90.80%. Fig. 5 shows the ROCs corresponding to the controlled and uncontrolled pairs. It is observed that humans find it harder to match the uncontrolled image pairs as compared to the con- trolled pairs. Thus it performed a one-tailed t-test to evaluate the null hypothesis that the performance for the controlled image pairs and the uncontrolled image pairs come from dis- tributions with equal mean. The resulting p-value is 0.0035 [7]. Thus, we have statistically significant evidence that the partic- ipants did better on the controlled pairs than on the uncon- trolled pairs. So presence of external factors like illumination, etc. tend to make the already challenging problem of distin- guishing identical twin siblings even more difficult.

5.3 What are the types of features that humans consid- er important to distinguish between identical twins?

In this study, given a pair of facial images, the participants were asked to choose the facial features which helped them in making their decision. Fig. 6 shows which feature types were chosen as important for the correct as well as incorrect re- sponses. From the figure, it is evident that for the correct re- sponses, the most important feature type chosen is moles/scars/freckles and it is significantly more important than any of the other types. For the incorrect responses, none of the features seem significantly more important than the others. This observation suggests that humans are more likely to be incorrect in their decision if they cannot find moles/scars/freckles in the images.
Fig. 6. Useful feature for distinguishing image of identical

twin siblings.

Now we investigate the ability of automatic face matching
algorithms to distinguish between identical twins. We experi-

mented with two commercial matchers (Pittpatt [12] and Cog- nitec [13]) in addition to standard holistic face matching ap- proaches based on Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). The algorithms were tested for the same 100 pairs used in the second experiment. Both the traditional matchers and Pittpatt performed verypoorly in this task. This result is in agreement with recent research [1] that has shown that currently available face recognition algorithms perform poorly on facial images of identical twins. Only Cognitec matcher performed comparable to humans and Fig. 8 shows the ROC obtained. The average human performance (computed from all the 25 participants) is also shown for comparison. As can be seen from Fig. 8, human observers outperform the automatic matcher for almost the entire range of False Accept Rate (FAR).
Fig. 8. Comparision of human performance against a commercial face recognition engine

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Author further investigate if the automatic matcher and humans show similar behaviour with regard to difficulty in distinguishing between identical twins. If algorithms and hu- mans behave differently, human expertise can potentially be used to guide development of better computer algorithms. Author analysed the non-match pairs that were found to be most difficult (corresponding to the highest similarity scores) by the automatic algorithm. Two non-match image pairs which got the highest similarity scores (greater than 0.9995) in the automatic experiment. But humans did reasonably well in distinguishing between these identical twin pairs (accuracy of
88% and 100% respectively). This indicates that human ob-
servers were able to capture facial characteristics different from the automatic algorithm that helped them do well on these pairs [7]. Author noticed that these image pairs differ in moles/freckles distribution and author conjecture that this may be a reason for the good performance of the humans. Therefore, one potential way to incorporate human knowledge to improve machine performance is to robustly detect facial marks and use them for facial characterization in addition to existing feature set used by automatic algorithms.

In this paper we can conclude that there no any automated system or program or application is available that can 100% identify or find the match in twins. So with the reference of this paper I will look forward to implement a system for iden- tifying solution to find the exact match between the twins. All above experimental results is being taken from a study of Face Recognition of Identical Twins by Human.

[9] E. Meyers and L. Wolf, "Using biologically inspired features for face processing," International Journal of Computer Vision, vol. 76, no. 1, pp.

93-104, January 2008.

[10] Y. M. Lui, D. Bolme, B. A. Draper, J. R. Beveridge, G. Givens, and P. Phillips, "A meta-analysis of face recognition covariates," in IEEE Interna- tional Conf. On Biometrics: Theory, Applications And Systems, 2009, pp.

1-8.

[11] "Twins days festival official website," http://www.twinsdays.org/. [12] "Pittsburgh pattern recognition," http://www.pittpatt.com/.

[13] "Cognitec: The face recognition company," http://www.cognitec- systems.de/.

REFERENCES

[1] Z. Sun, A. A. Paulino, J. Feng, Z. Chai, T. Tan, and A. K. Jain, "A study of multibiometric traits of identical twins," in Proc. SPIE, Biometric Tech- nology for Human Identification VII, April 2010.

[2] A. J. O'Toole, P. J. Phillips, F. Jiang, J. Ayyad, N. Pnard, and H. Abdi, "Face recognition algorithms surpass humans matching faces across changes in illumination," IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, vol. 29, no. 9, pp. 1642-1646, September 2007.

[3] N. Ye and T. Sim, "Combining facial appearance and dynamics for face recognition," LNCS, vol. 5702, pp. 133-140, 2009.

[4] K. Kodate, R. Inaba, E. Watanabe, and T. Watanabe, "Facial recognition by a compact parallel optical correlator," Measurement Science and Tech- nology, vol. 13, pp. 1756-1766, 2002.

[5] U. Park and A. Jain, "Face matching and retrieval using soft biomet- rics," IEEE Transactions on Information Forensics and Security, vol. 5, no.

3, pp. 406-415, September 2010.

[6] K. Hollingsworth, K. Bowyer, and P. Flynn, "Similarity of iris texture between identical twins," in IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2010, pp. 22-29.

[7] Soma Biswas, Kevin W. Bowyer and Patrik J. Flynn, Dept. of Computer Science and Engg., University of Notre Dame, A Study of Face Recogni- tion of Identical Twins by Humans.

[8] A. Ariyaeeiniaa, C. Morrison, A. Malegaonkara, and B. Black, "A test of the effectiveness of speaker verification for differentiating between identi- cal twins," Science and Justice, vol. 48, no. 4, pp. 182-186, December 2008

IJSER © 2015 http://www.ijser.org

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A Survey on Face Recognition of Identical Twins

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International Journal of Scientific & Engineering Research, Volume 6, Issue 2, February-2015 744 ISSN 2229-5518 A Survey on Face Recognition of Identical Twins Kanchan Patil, Prof. Sachin Bojewar Abstract - Recent studies have shown that face recognition performance degrades considerably for images of identical twins.

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| John

| Ana

| Carmen

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| Endo

| Leonel

| Candy

| Azita

| Alin

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Wordwall - Create better lessons quicker

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To tamper with - To alter or change in some way, To forge - To produce an imitation of a document, To make a call on (a face) - To make a decision, To come down to - To be dependent on, To breach security - To break rules, To go against your intuition - Your instinctive/gut feeling , To put a spanner in the works - Do s'thing that stops work going ahead, To be flagged as suspicious - To be noticed, To catch someone out - Detect someone doing something wrong, To dub someone - To name someone, A doppelganger - Someone who looks identical, To roll out - To introduce ( a new process),

Similar activities from Community

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To tamper with - To alter or change in some way, To forge - To produce an imitation of a document, To make a call on (a face) - To make a decision, To come down to - To be dependent on, To breach security - To break rules, To

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How does immigration work in Australia? BBC News

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Interview your partner, based on this shortened example decide if your partner is eligible to migrate.

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Popular Videos - Border Security: Australia's Front Line

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Follow the work of the men and women who protect Canada's borders. Border Security Canada Season 3 ep 10 11 12 13 14 Canada's Front Line. Border .

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Game: Hangman

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This is a user created hangman game. All words have been set by an anonymous user.

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Student Book p.19 - TASK: A/B/C Breakout Rooms

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Student Book p.19 - TASK: A/B/C Breakout Rooms

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1. One person in each Breakout Room: download this picture
2. "Share Screen" in your groups
3. Annotate your answers from the listening together.

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Barack Obama - "American Jobs Act" speech

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Barack Obama - "American Jobs Act" speech

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President Obama Presents American Jobs Act

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Martin Luther King - Famous speech: "I have a dream"

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Martin Luther King - Famous speech: "I have a dream"

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Start from [11 mins 27sec] to match the section in the Student Booklet p.19

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King's "I Have a Dream" Speech

Freedom's Ring is Martin Luther King, Jr.'s "I Have a Dream" speech, animated. Here you can compare the written and spoken speech, explore multimedia images, listen to movement activists, and uncover historical context. Fifty years ago, as the culminating address of the March on Washington for Jobs and Freedom, King demanded the riches of freedom and the security of justice. Today, his language of love, nonviolent direct action, and redemptive suffering resonates globally in the millions who stand up for freedom together and elevate democracy to its ideals. How do the echoes of King's Dream live within you?

Credits & Acknowledgements

Director, Art and Content: Evan Bissell
Design and Programming: Erik Loyer
Content, Curriculum Design and Project Coordinator: Andrea McEvoy Spero
Project Advisor: Clayborne Carson
Video: Owen Bissell
Project Administration: Regina Covington

Freedom's Ring is a project of The Martin Luther King, Jr. Research and Education Institute at Stanford University in collaboration with Beacon Press's King Legacy Series.

We extend our deep appreciation to the many people whose work and lives contributed to Freedom's Ring. Thank you to the interviewees: Aldo Billingslea, Clayborne Carson, Dorothy Cotton, Miriam Glickman, Kazu Haga, Bruce Hartford, Ericka Huggins, Clarence B. Jones, Kim Nalley, Wazir Peacock, and Marcus Shelby.

Thank you to Tenisha Armstrong. Her dedication and tireless efforts in editing Dr. King's papers allow us to make this history available to teachers and students.

Thank you to the many photographers whose work has inspired much of this project and allowed these important histories to continue. We have made our best efforts to credit these photographers. They include: Bob Adelman, Eve Arnold, George Ballis, Martha Cooper, Benedict Fernandez, Bob Fitch, Declan Haun, Matt Herron, John Loengard, Danny Lyon, Spider Martin, Charles Moore/Black Star, Herbert Randall, Steve Schapiro, Flip Schulke, Maria Varela, and Tamio Wakayama.

Thank you to David Stein for his invaluable contributions and conversations about this history. Thanks to Lucas Guilkey for his work on the videos, Ming-kuo Hung for editing support, and Naomi Wilson for her comments on content.

Thanks to Beacon Press for editing support.

Thanks to Headlands Center for the Arts for the time and space to finish the project.

The Martin Luther King, Jr. Research and Education Institute Staff:

Clayborne Carson
Director

Tenisha Armstrong
Associate Director and Editor of the King Papers Project

Regina Covington
Administrator

Andrea McEvoy Spero
Director of Education

Clarence B. Jones
Scholar in Residence

Susan Carson
Editorial Consultant

Stacey Zwald
Assistant Editor

Dave Beals
Research Assistant

Video hosting by Critical Commons
Content management by Scalar,
a project of the Alliance for Networking Visual Culture

Works Cited

Baldwin, James. The Fire Next Time. New York: Dell, 1985. Print.

Branch, Taylor. Parting the Waters: America in the King years, 1954-63. New York : Simon and Schuster, 1988. Print.

Carson, Clayborne, eds. The Autobiography of Martin Luther King, Jr.. New York: Intellectual Properties Management; Warner Books, 1998. Print.

Freed, Leonard. This Is the Day: The March on Washington. Los Angeles: J. Paul Getty Museum, 2013. Print.

Hansen, Drew D.. The Dream: Martin Luther King, Jr., and the Speech that Inspired a Nation. New York : Ecco. 2003. Print.

Johnson, Charles and Bob Adelman. King: The Photobiography of Martin Luther King, Jr. New York : Viking, 2000. Print.

Jones, Clarence B. and Stuart Connelly. Behind the Dream: The Making of the Speech that Transformed a Nation. New York: Palgrave Macmillan, 2011. Print.

Jones, William P.. The March on Washington: Jobs, Freedom and the Forgotten History of Civil Rights. New York : W.W. Norton, 2013. Print.

Jones, William P. and Labor and Working-Class History Association. "The Unknown Origins of the March on Washington: Civil Rights Politics and the Black Working Class." Labor: Studies in Working-Class History of the Americas 7.3 (2010). Print.

Kasher, Steven. The Civil Rights Movement: A Photographic History, 1954-68. New York: Abbeville, 1996. Print.

Katznelson, Ira. Fear Itself: The New Deal and the Origins of Our Time. New York: Liveright, 2013. Print.

Kelen, Leslie G., eds. This Light of Ours: Activist Photographers of the Civil Rights Movement. Jackson : University Press of Mississippi, 2011. Print.

LaFayette, Jr., Bernard and David C. Jehnsen. The Nonviolence Briefing Booklet: A 2-Day Orientation to Kingian Nonviolence Conflict Reconciliation. 1995. Galena: Institute for Human Rights and Responsibilities. 2007. Print.

Le Blanc, Paul. "Freedom Budget: The Promise of the Civil Rights Movement for Economic Justice." WorkingUSA: The Journal of Labor & Society 16 (2013), 43-58. Print.

Levine, Ellen. Freedom's Children : Young Civil Rights Activists Tell Their Own Stories. New York : Putnam, 1993. Print.

Lewis, John and Michael D'Orso. Walking with the Wind: A Memoir of the Movement. New York: Simon and Schuster, 1998. Print.

Sundquist, Eric J.. King's Dream. New Haven : Yale University, 2009. Print.

# data[].attributes.attachment_link.description
Freedom's Ring is Martin Luther King, Jr.'s "I Have a Dream" speech, animated. Here you can compare the written and spoken speech, explore multimedia images, listen to movement activists, and uncover historical context. Fifty years ago, as the culminating address of the March on Washington for Jobs and Freedom, King demanded the riches of freedom and the security of justice.

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Martin Luther King - "I have a dream" speech (part; not full)

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Martin Luther King - "I have a dream" speech (part; not full)

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Video included, but not full version in Student Booklet.

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I Have a Dream speech by Martin Luther King .Jr HD (subtitled)

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Full Presentation of work in Breakout Rooms

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Full Presentation of work in Breakout Rooms

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Presentations  - taking someone on your journey

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Listening Challenge - answer the questions as you watch the TED talk.

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Listening Challenge - answer the questions as you watch the TED talk.

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TED TALK: "Key to Success? Grit" by Amanda Lee Duckworth
https://api.playposit.com/go/share/35933/1238045/0/0/Key-to-success-Grit

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Class Activity: Presentation - Brainstorming Ideas as a group

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Class Activity: Presentation - Brainstorming Ideas as a group

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Vocabulary Review

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This is a user created hangman game. All words have been set by an anonymous user.

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How to give a memorable presentation with one sentence

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https://docs.google.com/presentation/d/1HrnHm5lsLXWlfTWdjMChDcU_ekRrLyu-CCDmJBCBZ2U/edit?usp=sharing

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https://docs.google.com/presentation/d/1BAVGPStqISJWir41GZlWUMjxMTixx7Pq2_agbVMCrAI/edit?usp=sharing

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https://docs.google.com/presentation/d/1dNaMcdhVjVm1gsKls9GvqDrY7WZjGYTqkltkSZhKhmg/edit?usp=sharing

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https://docs.google.com/presentation/d/1CpmbJHO5Sl8J2hxkzY8NrCm6h7rKp1fW5IO7oq03A1Y/edit?usp=sharing

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From Julie

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Australian Citizenship Changes - Behind the News

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Your Teacher - Tuesday, Wednesday, Friday

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Your Teacher - Tuesday, Wednesday, Friday

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Hi Everyone! My name is Stacey and I'll be co-teaching with Julie on this DHA program. Apart from teaching, I also love travelling. I have been to 59 countries around the world so I may have passed through your country before. Looking forward to seeing your smiling faces for the next 2 weeks!  My email is:
s.mulcahy@icte.uq.edu.au if you need to get in touch.

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From Julie

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Why is citizenship important? - Behind the News

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Hi Team.
I'm Julie and I'm teaching on the DHA programme with Stacey. I've been teaching English for a very long time and I absolutely love my job! I'm looking forward to meeting you all and getting to know you! (I'll post a picture when I find a good one!) :-)