To counter the terrorist and narco terror activities in the Union Territory of Jammu and Kashmir, the police are taking full advantage of AI. They have introduced the use of GPS tracker anklets and an Artificial Intelligence-based facial recognition system to monitor the activities of listed criminals and terrorists in the Union Territory.&nbsp;

In a significant move, the Jammu and Kashmir Police has introduced an Artificial Intelligence-based facial recognition system at Navyug Checking Plaza, Banihal and also implemented the use of GPS tracker anklets for monitoring individuals accused in NDPS cases in Kupwara in North Kashmir. It is the first time that such tracker anklets have been used in the Kashmir Region, earlier it was one accused in Jammu that was put on the GPS tracker anklets monitoring.&nbsp;

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Jammu and Kashmir Police with the aim of enhancing surveillance capabilities and effectively combating drug-related offenses in bordering districts of North Kashmir have deployed GPS tracker anklets on two bailed-out accused as part of their bail conditions.&nbsp;

''Under the directive of the Kupwara Court, GPS tracker anklets have been deployed on two bailed-out accused, Abdul Majid Bhat and Aabid Ali Bhat, as part of their bail conditions. It's a high-tech surveillance system where we deploy these anklets and we keep monitoring them. If we doubt their movement, we make sure to tackle it. A police team ensured the successful implementation of this electronic surveillance technology, demonstrating the Police commitment to utilizing advanced tools in the fight against drug trafficking and related crimes. This proactive approach not only strengthens monitoring capabilities but also contributes to creating a safer environment for all residents of Kupwara, '' said G M Bhat, Addl SP Kupwara.&nbsp;

Jammu and Kashmir Police recently tweeted that they have activated an advanced AI-based facial recognition system at the Navyuga tunnel on the Srinagar-Jammu National Highway, marking a significant stride towards bolstering security measures on one of the most strategic NHWs in the UT. This technology is designed to identify and track individuals involved in criminal activities, including terrorists, over-ground workers (OGWs), absconders, history sheeters, thieves, and drug peddlers.&nbsp;

Also read:&nbsp;<a href="https://www.wionews.com/india-news/rohith-vemula-case-fake-caste-certificate-telangana-police-close-case-issue-clean-chit-to-accused-717870">Rohith Vemula case: ‘Fake caste certificate,’ Telangana police close case, issue clean chit to accused</a>

''In a significant stride towards bolstering security measures, Ramban Police operationalises Al-Based Facial Recognition (FR) System at Navyuga Checking Plaza, Banihal, '' said district Police Ramban on social media.&nbsp;

The advance AI-based facial recognition system has been set up and activated at the Navyuga tunnel’s checkpoint in Banihal. The system's implementation marks a significant change in smart policing initiatives.

To counter the terrorist and narco terror activities in the Union Territory of Jammu and Kashmir, the police are taking full advantage of AI. They have introduced the use of GPS tracker anklets and an Artificial Intelligence-based facial recognition system to monitor the activities of listed criminals and terrorists in the Union Territory.

To counter the terrorist and narco terror activities in the Union Territory of Jammu and Kashmir, the police are taking full advantage of AI. They have introduced the use of GPS tracker anklets and an Artificial Intelligence-based facial recognition system to monitor the activities of listed criminals and terrorists in the Union Territory. In a significant move, the Jammu and Kashmir Police has introduced an Artificial Intelligence-based facial recognition system at Navyug Checking Plaza, Banihal and also implemented the use of GPS tracker anklets for monitoring individuals accused in NDPS cases in Kupwara in North Kashmir. It is the first time that such tracker anklets have been used in the Kashmir Region, earlier it was one accused in Jammu that was put on the GPS tracker anklets monitoring. Also read: BJP’s Hindutva regressive, our Hindutva reformist, says Uddhav Thackeray Jammu and Kashmir Police with the aim of enhancing surveillance capabilities and effectively combating drug-related offenses in bordering districts of North Kashmir have deployed GPS tracker anklets on two bailed-out accused as part of their bail conditions. ''Under the directive of the Kupwara Court, GPS tracker anklets have been deployed on two bailed-out accused, Abdul Majid Bhat and Aabid Ali Bhat, as part of their bail conditions. It's a high-tech surveillance system where we deploy these anklets and we keep monitoring them. If we doubt their movement, we make sure to tackle it. A police team ensured the successful implementation of this electronic surveillance technology, demonstrating the Police commitment to utilizing advanced tools in the fight against drug trafficking and related crimes. This proactive approach not only strengthens monitoring capabilities but also contributes to creating a safer environment for all residents of Kupwara, '' said G M Bhat, Addl SP Kupwara. Jammu and Kashmir Police recently tweeted that they have activated an advanced AI-based facial recognition system at the Navyuga tunnel on the Srinagar-Jammu National Highway, marking a significant stride towards bolstering security measures on one of the most strategic NHWs in the UT. This technology is designed to identify and track individuals involved in criminal activities, including terrorists, over-ground workers (OGWs), absconders, history sheeters, thieves, and drug peddlers. Also read: Rohith Vemula case: ‘Fake caste certificate,’ Telangana police close case, issue clean chit to accused ''In a significant stride towards bolstering security measures, Ramban Police operationalises Al-Based Facial Recognition (FR) System at Navyuga Checking Plaza, Banihal, '' said district Police Ramban on social media. The advance AI-based facial recognition system has been set up and activated at the Navyuga tunnel’s checkpoint in Banihal. The system's implementation marks a significant change in smart policing initiatives.

J&K police activates AI-based facial recognition system and GPS tracker anklets to monitor criminals

Tech experts and activists have been criticizing the use of facial recognition technology saying that it will only increase racial discrimination.

Facial recognition less accurate for people of colour

The Iranian government is looking to implement a new technology which will allow them to identify the women who are not complying with the country’s tough new law which makes wearing hijabs in public compulsory.&nbsp; The women breaking the law signed by President Ebrahim Raisi will be recognised with the help of&nbsp;face recognition technology, Mohammad Saleh Hashemi Golpayegani, the secretary of Iran’s Headquarters for Promoting Virtue and Preventing Vice, said according to The Guardian.&nbsp;

The new law was signed on August 15, more than a month on July 12 – the national “Hijab and Chastity Day” celebrated in Iran. The occasion saw widespread protests by women in the country and they were out in the streets without any head coverings resulting in a number of arrests and detentions.&nbsp;

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“The Iranian government has long played with the idea of using facial recognition to identify people who violate the law, Azadeh Akbari, a researcher at the University of Twente, told The Guardian. “The regime combines violent ‘old-fashioned’ forms of totalitarian control dressed up in new technologies.”&nbsp;

Iran currently issues biometric identity cards for their citizens which store information like iris scans and fingerprints. The announcement sparked fears among a number of people in the country that the same technology will now be used to facially identify dissenters and also snoop on the general public.&nbsp;

Also read |&nbsp;<a href="https://www.wionews.com/world/volodymyr-zelensky-claims-ukraine-takes-three-settlements-in-south-and-east-in-counter-offensive-512978">Ukraine takes three settlements in south and east in counter-offensive</a>

“A large chunk of the Iranian population is now in this national biometric data bank, as many public services are becoming dependent on biometric IDs.” &nbsp;

“So, the government has access to all the faces; they know where people come from and they can easily find them. A person in a viral video can be identified in seconds,” Akbari explained.&nbsp;

(With inputs from agencies)

<iframe allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen="" frameborder="0" height="350" loading="”lazy”" src="https://www.wionews.com/live-tv/embed?autoplay=1&amp;mute=1" width="100%"></iframe>

The Iranian government is looking to implement a new technology which will allow them to identify the women who are not complying with the country’s tough new law which makes wearing hijabs in public compulsory.

The Iranian government is looking to implement a new technology which will allow them to identify the women who are not complying with the country’s tough new law which makes wearing hijabs in public compulsory. The women breaking the law signed by President Ebrahim Raisi will be recognised with the help of face recognition technology, Mohammad Saleh Hashemi Golpayegani, the secretary of Iran’s Headquarters for Promoting Virtue and Preventing Vice, said according to The Guardian. The new law was signed on August 15, more than a month on July 12 – the national “Hijab and Chastity Day” celebrated in Iran. The occasion saw widespread protests by women in the country and they were out in the streets without any head coverings resulting in a number of arrests and detentions. Also read | Canada: 10 killed in mass stabbings. All you need to know about the incident “The Iranian government has long played with the idea of using facial recognition to identify people who violate the law, Azadeh Akbari, a researcher at the University of Twente, told The Guardian. “The regime combines violent ‘old-fashioned’ forms of totalitarian control dressed up in new technologies.” Iran currently issues biometric identity cards for their citizens which store information like iris scans and fingerprints. The announcement sparked fears among a number of people in the country that the same technology will now be used to facially identify dissenters and also snoop on the general public. Also read | Ukraine takes three settlements in south and east in counter-offensive “A large chunk of the Iranian population is now in this national biometric data bank, as many public services are becoming dependent on biometric IDs.” “So, the government has access to all the faces; they know where people come from and they can easily find them. A person in a viral video can be identified in seconds,” Akbari explained. (With inputs from agencies)

Iran plans to use surveillance technology to enforce new hijab law

Facebook has announced that it is deleting about 1 billion face prints, it uses as a part of a facial recognition system, citing concerns over technology.

Facebook shuts down automatic face recognition, says 'concerns outweigh benefits'

Amazon said on Tuesday, it is extending its ban on the use of its facial recognition tools for law enforcement, amid persistent concerns of bias in the technology.&nbsp;

The tech giant in last June announced, it was implementing a one-year moratorium on police use of its technology, saying the pause could give Congress time to enact safeguards against misuse of facial recognition.&nbsp;

The company confirmed on Tuesday an extension of that ban 'until further notice' without elaborating.&nbsp;

Last year's move came amid widespread protests over police brutality and concerns that facial recognition technology is flawed, especially in analysing features of African Americans.&nbsp;

Activists also say the technology tools may use algorithms, which discriminate, intentionally or not, against Black people.&nbsp;

Activists have targeted Amazon Web Services cloud computing unit's 'Rekognition' facial recognition technology and Ring surveillance cameras used for home security. It was not clear the degree to which police have used the systems.&nbsp;

Amazon last year called for governments to put in place ‘stronger regulations to govern the ethical use of facial recognition technology’.&nbsp;

Its ban was followed by Microsoft and IBM, which made similar announcements.&nbsp;

Last week, a coalition of activist groups called for Amazon to permanently stop selling its Rekognition system and on Tuesday said, the company should commit to scrapping the system.&nbsp;

"Facial recognition technology is too dangerous for it to be implemented at the whims of corporations like Amazon," said Evan Greer of the activist group, Fight for the Future, one of the groups in the coalition.&nbsp;

(With inputs from agencies)

Amazon said on Tuesday, it is extending its ban on the use of its facial recognition tools for law enforcement, amid persistent concerns of bias in the technology.

Amazon said on Tuesday, it is extending its ban on the use of its facial recognition tools for law enforcement, amid persistent concerns of bias in the technology. The tech giant in last June announced, it was implementing a one-year moratorium on police use of its technology, saying the pause could give Congress time to enact safeguards against misuse of facial recognition. The company confirmed on Tuesday an extension of that ban 'until further notice' without elaborating. Last year's move came amid widespread protests over police brutality and concerns that facial recognition technology is flawed, especially in analysing features of African Americans. Activists also say the technology tools may use algorithms, which discriminate, intentionally or not, against Black people. Activists have targeted Amazon Web Services cloud computing unit's 'Rekognition' facial recognition technology and Ring surveillance cameras used for home security. It was not clear the degree to which police have used the systems. Amazon last year called for governments to put in place ‘stronger regulations to govern the ethical use of facial recognition technology’. Its ban was followed by Microsoft and IBM, which made similar announcements. Last week, a coalition of activist groups called for Amazon to permanently stop selling its Rekognition system and on Tuesday said, the company should commit to scrapping the system. "Facial recognition technology is too dangerous for it to be implemented at the whims of corporations like Amazon," said Evan Greer of the activist group, Fight for the Future, one of the groups in the coalition. (With inputs from agencies)

Amazon to extend ban on police use of face recognition tech 

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem.

A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park. They also happened to be seeking a project to hone their machine learning expertise.

“We thought, machine learning is really great at identifying people, what could it do for bears?” Miller said. Could artificial intelligence used for face recognition be harnessed to discern one bear face from another?

At Knight Inlet in British Columbia, Canada, Melanie Clapham was pondering the same question. Clapham, a postdoctoral researcher at the University of Victoria working with Chris Darimont of the Raincoast Conservation Foundation, was keen to explore face recognition technology as an aid to her grizzly bear studies. But her expertise was bear biology, not AI.

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Fortuitously, the four found a match on Wildlabs.net, an online broker of collaborations between technologists and conservationists. Combining their skill sets, Miller and Nguyen volunteered spare time over several years for this passion project that would eventually bear fruit, reporting the results of their experiment last week in the journal Ecology and Evolution. The project they produced, BearID, could help conservationists monitor the health of bear populations in various parts of the world, and perhaps aid work with other animals, too.

They got started by looking for other animals that had gotten the deep learning treatment.

“In typical engineering fashion, we’re always looking for a shortcut,” Miller said.

They discovered “dog hipsterizer,” a program that found the faces, eyes and noses of dogs in photos and placed rimmed glasses and mustaches on them. “That was where we started,” Nguyen said.

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Although trained on dogs, dog hipsterizer worked reasonably well on the similarly shaped faces of bears, giving them a programming head start. Nevertheless, Nguyen said, the work’s initial stages were tedious. Creating a training data set for the deep learning program involved examining over 4,000 photos with bears in them and then manually highlighting each bear’s eyes, nose and ears by drawing boxes around them so the program could learn to find these features.

The system also had to overcome a challenge of brown bears’ physical appearance.

To monitor populations, “we have to be able to recognize individuals,” said Clapham. But bears don’t have any feature comparable to a fingerprint, such as a zebra’s stripes or a giraffe’s spots.

From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Alabama, and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work “unsupervised,” Clapham said, to see how well it could spot differences between known bears from 935 photographs.

First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features.

Also read |&nbsp;<a href="https://www.wionews.com/world/canadas-health-chief-fears-for-peoples-mental-health-as-alcohol-consumption-spikes-304145#:~:text=Canada's%20consumption%20of%20alcohol%20and,bigger%20problem%20of%20mental%20fatigue.">Canada's health chief fears for people's mental health as alcohol consumption spikes</a>

The system identified bears at an accuracy rate of 84%, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve.

But how does it actually tell those bears apart? Before the era of deep learning, “we tried to imagine how humans perceive faces and how we distinguish individuals,” said Alexander Loos, a research engineer at the Fraunhofer Institute for Digital Media Technology, in Germany, who was not involved in the study but has collaborated with Clapham in the past. Programmers would manually input face descriptors into a computer.

But with deep learning, programmers input the images into a neural network that figures out how best to identify individuals. “The network itself extracts the features,” Loos said, which is a huge advantage.

He also cautioned that, “It’s basically a black box. You don’t know what it’s doing,” and that if the data set being examined is unintentionally biased, certain errors can emerge.

For instance, if some bears are photographed more often in light than in dark conditions, the lighting difference can cause misclassification of the bears. (Data bias can be a problem in human facial recognition by AI, with misidentifications known to be more likely for people of color).

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Whatever BearID is really doing, Clapham, who recognizes many Knight Inlet bears by sight, was surprised and encouraged by where the program fell short.

“The bears that I confused, the network confused as well,” she said, suggesting that the app behaves similarly to the neural network in her brain.

However, this first release of BearID is just the start. She hopes the open-source application will become more accurate with more inputs, use and time.

The app is of great interest to the Knight Inlet Lodge in Glendale Cove, which has run grizzly bear tours for decades, and its current owner, the Nanwakolas Council, whose members come from First Nations of Canada.

“Fifteen years ago when we started doing land use planning, there was just one provincial bear health expert for the whole province,” said Kikaxklalagee / Dallas Smith, the president of Nanwakolas Council and a member of the Tlowitsis Nation. That hampered the Nations’ understanding of the health of bears on their territory. He said he felt excited that this “Jason Bourne-ish” technology would allow for more informed stewardship of bears. “We’re trying to make it a sustainable, limited footprint operation.”

And BearID may not stop with North American bears, as Clapham is already in conversation with others keen to use it for species like sloth bears, sun bears and Asiatic bears, as well as wolves.

“What we’d love is that one day we have somewhere where people can upload camera trap images and the system tells you not only what species you’ve seen, but also what individual you’ve seen,” and maybe its sex and age as well, she said.

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem. A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park.

Ed Miller and Mary Nguyen are Silicon Valley software developers by day, but moonlight at solving an unusually fuzzy problem. A few years ago the pair became mesmerized, like many of us, by an Alaskan webcam broadcasting brown bears from Katmai National Park. They also happened to be seeking a project to hone their machine learning expertise. “We thought, machine learning is really great at identifying people, what could it do for bears?” Miller said. Could artificial intelligence used for face recognition be harnessed to discern one bear face from another? At Knight Inlet in British Columbia, Canada, Melanie Clapham was pondering the same question. Clapham, a postdoctoral researcher at the University of Victoria working with Chris Darimont of the Raincoast Conservation Foundation, was keen to explore face recognition technology as an aid to her grizzly bear studies. But her expertise was bear biology, not AI. Also read| Reducing greenhouse gas emission even to zero won't stop global warming: Study Fortuitously, the four found a match on Wildlabs.net, an online broker of collaborations between technologists and conservationists. Combining their skill sets, Miller and Nguyen volunteered spare time over several years for this passion project that would eventually bear fruit, reporting the results of their experiment last week in the journal Ecology and Evolution. The project they produced, BearID, could help conservationists monitor the health of bear populations in various parts of the world, and perhaps aid work with other animals, too. They got started by looking for other animals that had gotten the deep learning treatment. “In typical engineering fashion, we’re always looking for a shortcut,” Miller said. They discovered “dog hipsterizer,” a program that found the faces, eyes and noses of dogs in photos and placed rimmed glasses and mustaches on them. “That was where we started,” Nguyen said. Also read| Researchers find method to rapidly clear alcohol from lungs by breathing Although trained on dogs, dog hipsterizer worked reasonably well on the similarly shaped faces of bears, giving them a programming head start. Nevertheless, Nguyen said, the work’s initial stages were tedious. Creating a training data set for the deep learning program involved examining over 4,000 photos with bears in them and then manually highlighting each bear’s eyes, nose and ears by drawing boxes around them so the program could learn to find these features. The system also had to overcome a challenge of brown bears’ physical appearance. To monitor populations, “we have to be able to recognize individuals,” said Clapham. But bears don’t have any feature comparable to a fingerprint, such as a zebra’s stripes or a giraffe’s spots. From 4,675 fully labeled bear faces on DSLR photographs, taken from research and bear-viewing sites at Brooks River, Alabama, and Knight Inlet, they randomly split images into training and testing data sets. Once trained from 3,740 bear faces, deep learning went to work “unsupervised,” Clapham said, to see how well it could spot differences between known bears from 935 photographs. First, the deep learning algorithm finds the bear face using distinctive landmarks like eyes, nose tip, ears and forehead top. Then the app rotates the face to extract, encode and classify facial features. Also read | Canada's health chief fears for people's mental health as alcohol consumption spikes The system identified bears at an accuracy rate of 84%, correctly distinguishing between known bears such as Lucky, Toffee, Flora and Steve. But how does it actually tell those bears apart? Before the era of deep learning, “we tried to imagine how humans perceive faces and how we distinguish individuals,” said Alexander Loos, a research engineer at the Fraunhofer Institute for Digital Media Technology, in Germany, who was not involved in the study but has collaborated with Clapham in the past. Programmers would manually input face descriptors into a computer. But with deep learning, programmers input the images into a neural network that figures out how best to identify individuals. “The network itself extracts the features,” Loos said, which is a huge advantage. He also cautioned that, “It’s basically a black box. You don’t know what it’s doing,” and that if the data set being examined is unintentionally biased, certain errors can emerge. For instance, if some bears are photographed more often in light than in dark conditions, the lighting difference can cause misclassification of the bears. (Data bias can be a problem in human facial recognition by AI, with misidentifications known to be more likely for people of color). Also read| 'Gene within gene': Scientists make worrying discovery about coronavirus nucleotides Whatever BearID is really doing, Clapham, who recognizes many Knight Inlet bears by sight, was surprised and encouraged by where the program fell short. “The bears that I confused, the network confused as well,” she said, suggesting that the app behaves similarly to the neural network in her brain. However, this first release of BearID is just the start. She hopes the open-source application will become more accurate with more inputs, use and time. The app is of great interest to the Knight Inlet Lodge in Glendale Cove, which has run grizzly bear tours for decades, and its current owner, the Nanwakolas Council, whose members come from First Nations of Canada. “Fifteen years ago when we started doing land use planning, there was just one provincial bear health expert for the whole province,” said Kikaxklalagee / Dallas Smith, the president of Nanwakolas Council and a member of the Tlowitsis Nation. That hampered the Nations’ understanding of the health of bears on their territory. He said he felt excited that this “Jason Bourne-ish” technology would allow for more informed stewardship of bears. “We’re trying to make it a sustainable, limited footprint operation.” And BearID may not stop with North American bears, as Clapham is already in conversation with others keen to use it for species like sloth bears, sun bears and Asiatic bears, as well as wolves. “What we’d love is that one day we have somewhere where people can upload camera trap images and the system tells you not only what species you’ve seen, but also what individual you’ve seen,” and maybe its sex and age as well, she said.

Training facial recognition on some new furry friends: Bears

Travellers will soon be able to enter airports in the country with facial recognition biometric under the government's Digi Yatra initiative.

Civil Aviation Minister Suresh Prabhu said the initiative would be "forward-looking and futuristic" and would be launched soon.

He also said it would be voluntary for passengers on whether to opt for facial recognition biometric for air travel.

Digi Yatra initiative seeks to promote paperless and hassle-free air travel.

It is biometrics-based digital processing system for passenger entry and related requirements at the airport.

The Digi Yatra platform would be operational by February 2019, according to the ministry.&nbsp;

Travellers will soon be able to enter airports in the country with facial recognition biometric under the government's Digi Yatra initiative. Civil Aviation Minister Suresh Prabhu said the initiative would be "forward-looking and futuristic" and would be launched soon.

Travellers will soon be able to enter airports in the country with facial recognition biometric under the government's Digi Yatra initiative. Civil Aviation Minister Suresh Prabhu said the initiative would be "forward-looking and futuristic" and would be launched soon. He also said it would be voluntary for passengers on whether to opt for facial recognition biometric for air travel. Digi Yatra initiative seeks to promote paperless and hassle-free air travel. It is biometrics-based digital processing system for passenger entry and related requirements at the airport. The Digi Yatra platform would be operational by February 2019, according to the ministry.

Soon, face recognition to allow flyers to enter airports: Civil Aviation Minister

Most parents can remember the subtle mix of excitement and anxiety accompanying the choice of their baby&rsquo;s name &ndash; it will follow the child his or her entire life. But the effect could be even more significant. In research recently published in the Journal of Personality and Social Psychology, our research team shows that the stereotype that a given society has of a first name can influence the way people look.

In eight studies, we found that participants shown ID-style photos of people they&rsquo;d never met were able to recognise the first name of the depicted person well above the chance level. For example, let&rsquo;s say you live in North America and are shown a picture of a woman whose name is Emily (you aren&rsquo;t given this information). Below her image four possible first names are listed: Claire, Deborah, Emily and Melissa. Assuming that the first names are equally common in North America in/around the year that the person was born, study participants should pick the correct name approximately 25% of the time &ndash; in other words, by chance. What we found is that participants typically choose the depicted person&rsquo;s true first name 35% to 40% of the time. In other words, there is something about an Emily that&hellip;just looks like an Emily.

If an Emily really does look like an Emily, even a computer should be able to guess her true name. And that is what we find, across dozens of names. We had a computer analyse almost 100,000 ID portraits of French people. Across the board, the computer was able to recognise the person&rsquo;s true first name above the rate of pure chance. In fact, the computer was even able to produce a &ldquo;heat map&rdquo; for each name, a face with the features that &ldquo;betray&rdquo; a person carrying that name shown in warm colours.&nbsp;

How should we understand this effect? Until now, social psychologists knew that our facial appearance influences the extent to which others perceive us as attractive, intelligent, trustworthy or warm. This study shows that others&rsquo; perceptions of our first name is reflected in our faces.

How is that possible? Our theory is that when a child receives a first name, it comes loaded with a number of social expectations, inferences and interactions. It is possible that this child be treated as though s/he has certain personality traits &ndash; for example, a Katherine may be a good student, while an Allison may be mischievous. The social attitude toward a first name, along with its continuous pressure on the person wearing that name, can influence the perception that the name-wearer has of themselves, and end up weighing on the development of their appearance. For instance, Allison may end up adopting a hairstyle, expressions or specific facial traits (eg. smile lines) consistent with the stereotype of an Allison that her cultural group has in mind.

Self-fulfilling prophecy is already well documented as far as personality is concerned. For example, given the stereotype that Chinese people tend to excel at mathematics, a Chinese woman being reminded of the fact she is Chinese tends to perceive herself as better at the subject compared to a Chinese woman being reminded of the fact that she is a woman. What is new with our research is it shows our tendency to adhere to social stereotypes ends up appearing on our face, at least via the social cues that our first name represents. This may happen either directly (e.g. Allison wears her hair down; Angelina ties it up) or work via the effect of a first name on people&rsquo;s personality (e.g. a first name may be associated with someone who is open-minded, which would then translate on a more open-looking face).

We therefore identify a type of Dorian Gray effect, from the lead protagonist in Oscar Wilde&rsquo;s novel, whose portrait evolves as he carries out dark acts. The fact that our first name, chosen by others for us, shapes our appearance in adulthood suggests a powerful social structuring that affects us from early on in our life.

Social structuring

The face-name effect &ndash; the fact that we recognise the first name of someone we don&rsquo;t know beyond chance level &ndash; does have its limits. These limits further illustrate the self-fulfilling mechanism behind it.

First of all, we find that individuals of a given culture display the face-name effect when looking at pictures of individuals from their culture, but no longer recognise the name when the pictures are of individuals from another culture. In one of our studies, we asked French and Israeli participants to recognise the true name of French and Israeli people they did not know. The face-name effect occurred for French people confronted with French faces, as well as for Israelis confronted with Israeli faces. It did not occur for French people confronted with Israeli faces or Israelis confronted with French faces. In other words, perceivers no longer recognised the depicted people&rsquo;s true name above chance level when the latter were not from their culture.

In another study we found that if the people depicted do not use their first name socially, but instead exclusively use a nickname (eg. Charlotte does not go by Charlotte, but by &ldquo;Chachou&rdquo;), the face-name effect disappears, and people no longer recognise Charlotte as a Charlotte. Again, this is consistent with the fact that the name-wearer undergoes a social pressure when she or he morphs their face to tend toward the social stereotype of their first name. When exclusively using a nickname, for which there is no shared stereotype, this pressure disappears.

Finally, the face-name effect occurs even if we can only see the hair of a person. Our hair is possibly the part of our face that we control with the most ease. The fact that this alone can produce the face-name effect further illustrates the suspected self-fulfilling mechanism behind it.

Together, the eight studies suggest that we wear our social belonging on our face, and that we actively shape our features to be recognised by our reference group. Choosing baby names remains exciting. Whatever the first name you give to your child, he or she will end up wearing it.&nbsp;

(Disclaimer: The opinions expressed above are the personal views of the author and do not reflect the views of ZMCL)

Eight studies have found that when people were shown ID-style photos of people they'd never met, they were often able to correctly select the person's first name.

Most parents can remember the subtle mix of excitement and anxiety accompanying the choice of their baby’s name – it will follow the child his or her entire life. But the effect could be even more significant. In research recently published in the Journal of Personality and Social Psychology, our research team shows that the stereotype that a given society has of a first name can influence the way people look. In eight studies, we found that participants shown ID-style photos of people they’d never met were able to recognise the first name of the depicted person well above the chance level. For example, let’s say you live in North America and are shown a picture of a woman whose name is Emily (you aren’t given this information). Below her image four possible first names are listed: Claire, Deborah, Emily and Melissa. Assuming that the first names are equally common in North America in/around the year that the person was born, study participants should pick the correct name approximately 25% of the time – in other words, by chance. What we found is that participants typically choose the depicted person’s true first name 35% to 40% of the time. In other words, there is something about an Emily that…just looks like an Emily. If an Emily really does look like an Emily, even a computer should be able to guess her true name. And that is what we find, across dozens of names. We had a computer analyse almost 100,000 ID portraits of French people. Across the board, the computer was able to recognise the person’s true first name above the rate of pure chance. In fact, the computer was even able to produce a “heat map” for each name, a face with the features that “betray” a person carrying that name shown in warm colours. How should we understand this effect? Until now, social psychologists knew that our facial appearance influences the extent to which others perceive us as attractive, intelligent, trustworthy or warm. This study shows that others’ perceptions of our first name is reflected in our faces. How is that possible? Our theory is that when a child receives a first name, it comes loaded with a number of social expectations, inferences and interactions. It is possible that this child be treated as though s/he has certain personality traits – for example, a Katherine may be a good student, while an Allison may be mischievous. The social attitude toward a first name, along with its continuous pressure on the person wearing that name, can influence the perception that the name-wearer has of themselves, and end up weighing on the development of their appearance. For instance, Allison may end up adopting a hairstyle, expressions or specific facial traits (eg. smile lines) consistent with the stereotype of an Allison that her cultural group has in mind. Self-fulfilling prophecy is already well documented as far as personality is concerned. For example, given the stereotype that Chinese people tend to excel at mathematics, a Chinese woman being reminded of the fact she is Chinese tends to perceive herself as better at the subject compared to a Chinese woman being reminded of the fact that she is a woman. What is new with our research is it shows our tendency to adhere to social stereotypes ends up appearing on our face, at least via the social cues that our first name represents. This may happen either directly (e.g. Allison wears her hair down; Angelina ties it up) or work via the effect of a first name on people’s personality (e.g. a first name may be associated with someone who is open-minded, which would then translate on a more open-looking face). We therefore identify a type of Dorian Gray effect, from the lead protagonist in Oscar Wilde’s novel, whose portrait evolves as he carries out dark acts. The fact that our first name, chosen by others for us, shapes our appearance in adulthood suggests a powerful social structuring that affects us from early on in our life. Social structuring The face-name effect – the fact that we recognise the first name of someone we don’t know beyond chance level – does have its limits. These limits further illustrate the self-fulfilling mechanism behind it. First of all, we find that individuals of a given culture display the face-name effect when looking at pictures of individuals from their culture, but no longer recognise the name when the pictures are of individuals from another culture. In one of our studies, we asked French and Israeli participants to recognise the true name of French and Israeli people they did not know. The face-name effect occurred for French people confronted with French faces, as well as for Israelis confronted with Israeli faces. It did not occur for French people confronted with Israeli faces or Israelis confronted with French faces. In other words, perceivers no longer recognised the depicted people’s true name above chance level when the latter were not from their culture. In another study we found that if the people depicted do not use their first name socially, but instead exclusively use a nickname (eg. Charlotte does not go by Charlotte, but by “Chachou”), the face-name effect disappears, and people no longer recognise Charlotte as a Charlotte. Again, this is consistent with the fact that the name-wearer undergoes a social pressure when she or he morphs their face to tend toward the social stereotype of their first name. When exclusively using a nickname, for which there is no shared stereotype, this pressure disappears. Finally, the face-name effect occurs even if we can only see the hair of a person. Our hair is possibly the part of our face that we control with the most ease. The fact that this alone can produce the face-name effect further illustrates the suspected self-fulfilling mechanism behind it. Together, the eight studies suggest that we wear our social belonging on our face, and that we actively shape our features to be recognised by our reference group. Choosing baby names remains exciting. Whatever the first name you give to your child, he or she will end up wearing it. (Disclaimer: The opinions expressed above are the personal views of the author and do not reflect the views of ZMCL)

Opinion: People's perceptions of our first name is reflected in our faces

The Unique Identification Authority of India said Monday that it would also allow for Aadhaar cardholders to be verified by face recognition.&nbsp;

&quot;At the time of Aadhaar enrolment, photo of the face of resident is also captured. To provide inclusive authentication, face photo can also be leveraged to verify the identity of a Aadhaar number holder,&quot; the UIDAI said in a statement.&nbsp; &nbsp;

The UIDAI currently uses iris scans, fingerprints, and&nbsp;one-time passwords (OTPs) to verify cardholders

There have been reports recently of people not being able to be authenticated by their fingerprints, because age and/ or manual labour has rubbed out over the years.&nbsp;

The UIDAI added however that face recognition would be allowed only in &quot;fusion mode&quot;; ie along with either an iris scan or a fingerpint verification.&nbsp;

The UIDAI said the face-recognition verification would be enabled from July onwards.&nbsp;

Also read:&nbsp;<a href="http://www.wionews.com/india-news/new-security-layer-in-aadhaar-is-like-locking-the-stable-after-horses-have-bolted-p-chidambaram-29179" target="_blank">New security layer in Aadhaar is like locking the stable after horses have bolted, says Chidambaram</a>

This does not however mean that Aadhaar cardholders will have to head back to their local UIDAI centres and get photographs of themselves taken. The UIDAI said Monday that since photographs are taken of people at the time they are applying for Aadhaar cards, they already have photographs of all Aadhaar cardholders.&nbsp;

The facial authentication is also expected to add a further layer of security to Aadhaar cardholders.&nbsp;

The UIDAI had announced recently that it would be issuing <a href="http://www.wionews.com/india-news/uidai-issues-virtual-id-against-aadhaar-to-strengthen-data-security-29128" target="_blank">virtual Aadhaar ID numbers</a> -- as an added security measure -- after a news report said Aadhaar details were being sold for as little as Rs 500.&nbsp;

The UIDAI had filed an FIR against the newspaper and the reporter who had filed the story.&nbsp;

The Unique Identification Authority of India currently uses iris scans, fingerprints, and one-time passwords to verify cardholders

The Unique Identification Authority of India said Monday that it would also allow for Aadhaar cardholders to be verified by face recognition. "At the time of Aadhaar enrolment, photo of the face of resident is also captured. To provide inclusive authentication, face photo can also be leveraged to verify the identity of a Aadhaar number holder," the UIDAI said in a statement. The UIDAI currently uses iris scans, fingerprints, and one-time passwords (OTPs) to verify cardholders There have been reports recently of people not being able to be authenticated by their fingerprints, because age and/ or manual labour has rubbed out over the years. The UIDAI added however that face recognition would be allowed only in "fusion mode"; ie along with either an iris scan or a fingerpint verification. The UIDAI said the face-recognition verification would be enabled from July onwards. Also read: New security layer in Aadhaar is like locking the stable after horses have bolted, says Chidambaram This does not however mean that Aadhaar cardholders will have to head back to their local UIDAI centres and get photographs of themselves taken. The UIDAI said Monday that since photographs are taken of people at the time they are applying for Aadhaar cards, they already have photographs of all Aadhaar cardholders. The facial authentication is also expected to add a further layer of security to Aadhaar cardholders. The UIDAI had announced recently that it would be issuing virtual Aadhaar ID numbers -- as an added security measure -- after a news report said Aadhaar details were being sold for as little as Rs 500. The UIDAI had filed an FIR against the newspaper and the reporter who had filed the story.

UIDAI says face recognition will be allowed to authenticate Aadhaar cardholders

According to a new research, scientists can reconstruct faces using DNA.

Experts claim that the genome consists all the information that defines one&#39;s identity.&nbsp;By training an algorithm to link genetic information with facial features and voices, researchers were able to identify the people.

The technological advancement will bring forward the argument of database anonymity and privacy. Research centers frequently collect samples of human genomes from thousands of people to study diseases. In most cases, the volunteers are promised anonymity.

However, the research by Proceedings of the National Academy and Sciences, claims that such anonymity might not be possible in future as the genome itself will be able to determine their facial identity.

On the positive side, the technology can be useful in identifying criminals by building mug shots using DNA samples found at the crime scene.

Researchers were able to match eight out of ten genome samples with their head shots--an 80 per cent success rate. The system was fed with data from only 1000 samples and more data is likely to improve the algorithm incredibly.

&quot;Just like Google and others now sell advertising to you based on what is in your searches and emails, you could get adverts targeted to you based on what&#39;s in your genome,&quot; Craig Venter from US-based company Human Longevity told Times Of India.

Using the pool of information, scientists will be able to figure out traits of individuals, which could open a vast range of business opportunities. Medical insurance ventures could use the data to find out what diseases a person is prone to and design premiums accordingly.

Experts claim that the genome consists all the information that defines one's identity.?By training an algorithm to link genetic information with facial features and voices, researchers were able to identify the people

According to a new research, scientists can reconstruct faces using DNA. Experts claim that the genome consists all the information that defines one's identity. By training an algorithm to link genetic information with facial features and voices, researchers were able to identify the people. The technological advancement will bring forward the argument of database anonymity and privacy. Research centers frequently collect samples of human genomes from thousands of people to study diseases. In most cases, the volunteers are promised anonymity. However, the research by Proceedings of the National Academy and Sciences, claims that such anonymity might not be possible in future as the genome itself will be able to determine their facial identity. On the positive side, the technology can be useful in identifying criminals by building mug shots using DNA samples found at the crime scene. Researchers were able to match eight out of ten genome samples with their head shots--an 80 per cent success rate. The system was fed with data from only 1000 samples and more data is likely to improve the algorithm incredibly. "Just like Google and others now sell advertising to you based on what is in your searches and emails, you could get adverts targeted to you based on what's in your genome," Craig Venter from US-based company Human Longevity told Times Of India. Using the pool of information, scientists will be able to figure out traits of individuals, which could open a vast range of business opportunities. Medical insurance ventures could use the data to find out what diseases a person is prone to and design premiums accordingly.

A new technology challenges the anonymity of genetic databases

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