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Recognition at last
July 08, 2009Face recognition computers can see through your disguise
A rapid but superior method for computerized face recognition could revolutionize security systems especially if it can see through disguises, according to research published in this month's issue of the International Journal of Intelligent Systems Technologies and Applications.
Every face has special features that define that person, yet faces can also be very similar, explains Lin Huang, of Florida Atlantic University, in Boca Raton. That makes computerized face recognition for security and other applications an interesting but difficult task.
Face recognition software has been in development for many years. However, for biometric authentication at border crossings, for access to buildings, for automated banking, crime investigation, and other applications, has not yet become a mainstream application. The main technical limitation is although the systems are accurate they require a lot of computer power.
Early face recognition systems simply marked major facial features - eyes, nose mouth - on a photograph and computed the distances from these features to a common reference point. In the 1970s, a more automated approach using a facial template extended this idea to map the individual face on to a global template. By the 1980s, an almost entirely statistical approach led to the first fully automated face recognition system.
In the late 1980s researchers at Brown University developed the so-called "eigenface method", which was extended by a team at MIT in the early 1990s. Since then, approaches based on neural networks, dynamic link architectures (DLA), fisher linear discriminant model (FLD), hidden Markov models and Gabor wavelets. Then a way to create a ghost-like image that would succumb to an even more powerful analysis was developed that could accurately identify the majority of differences between faces.
However, powerful techniques have so far required powerful computers. Now, Huang and colleagues Hanqi Zhuang and Salvatore Morgera in the Department of Electrical Engineering, have applied a one-dimensional filter to the two-dimensional data from conventional analyses, such as the Gabor method. This allows them to reduce significantly the amount of computer power required without compromising accuracy.
The team tested the performance of their new algorithm on a standard database of 400 images of 40 subjects. Images are grey scale and just 92 x 112 pixels in size. They found that their technique is not only faster and works with low resolution images, such as those produced by standard CCTV cameras, but also solves the variation problems caused by different light levels and shadows, viewing direction, pose, and facial expressions. It can even see through certain types of disguises such as facial hair and glasses.
Inderscience Publishers
Face recognition software has been in development for many years. However, for biometric authentication at border crossings, for access to buildings, for automated banking, crime investigation, and other applications, has not yet become a mainstream application. The main technical limitation is although the systems are accurate they require a lot of computer power.
Early face recognition systems simply marked major facial features - eyes, nose mouth - on a photograph and computed the distances from these features to a common reference point. In the 1970s, a more automated approach using a facial template extended this idea to map the individual face on to a global template. By the 1980s, an almost entirely statistical approach led to the first fully automated face recognition system.
In the late 1980s researchers at Brown University developed the so-called "eigenface method", which was extended by a team at MIT in the early 1990s. Since then, approaches based on neural networks, dynamic link architectures (DLA), fisher linear discriminant model (FLD), hidden Markov models and Gabor wavelets. Then a way to create a ghost-like image that would succumb to an even more powerful analysis was developed that could accurately identify the majority of differences between faces.
However, powerful techniques have so far required powerful computers. Now, Huang and colleagues Hanqi Zhuang and Salvatore Morgera in the Department of Electrical Engineering, have applied a one-dimensional filter to the two-dimensional data from conventional analyses, such as the Gabor method. This allows them to reduce significantly the amount of computer power required without compromising accuracy.
The team tested the performance of their new algorithm on a standard database of 400 images of 40 subjects. Images are grey scale and just 92 x 112 pixels in size. They found that their technique is not only faster and works with low resolution images, such as those produced by standard CCTV cameras, but also solves the variation problems caused by different light levels and shadows, viewing direction, pose, and facial expressions. It can even see through certain types of disguises such as facial hair and glasses.
Inderscience Publishers
Face Recognition Current Events and Face Recognition News RSSImproving security with face recognition technology
A number of U.S. states now use facial recognition technology when issuing drivers licenses. Similar methods are also used to grant access to buildings and to verify the identities of international travelers. Historically, obtaining accurate results with this type of technology has been a time intensive activity.
BioVault locks up biometrics
A system that allows biometric data to be used to create a secret key for data encryption has been developed by researchers in South Africa.
'Super-recognizers,' with extraordinary face recognition ability, never forget a face
Some people say they never forget a face, a claim now bolstered by psychologists at Harvard University who've discovered a group they call "super-recognizers": those who can easily recognize someone they met in passing, even many years later.
New insight into how bees see
New research from Monash University bee researcher Adrian Dyer could lead to improved artificial intelligence systems and computer programs for facial recognition.
Are you phonagnosic?
The first known case of someone born without the ability to recognise voices has been reported in a paper by UCL (University College London) researchers, in a study of a rare condition known as phonagnosia. The UCL team are calling for other people to come forward if they think they have also grown up with the condition.
The Nose Knows: Two Fixation Points Needed for Face Recognition
Many of us are bad at remembering names but we are very quick to point out that at least we never forget a face.
Child witnesses -- how to improve their performance
A study at the University of Leicester into how to improve child and young adult witnesses' evidence has looked at several issues that affect witnesses' accuracy.
Face recognition: nurture not nature
Reporting in the open-access journal PLoS ONE on August 20, researchers have discovered that our society can influence the way we recognise other people's faces.
Face facts: People don't stand out in crowds
Why is it difficult to pick out even a familiar face in a crowd? We all experience this, but the phenomenon has been poorly understood until now.
Twins study shows genetic basis for face and place recognition
New evidence suggests our brains are hardwired before birth to recognize faces and places. But in contrast, the neural circuitry we use to recognize words develops mainly as a result of experience.
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fXGuard Windows Logon Computer Access Control with Face Recognition Technology by RCG The latest face recognition software specially designed for computers. With FxGuard your face is the login ID and password o access your PC/Notebook. More secure than passwords that can be forgotten or stolen. Comes with free USB camera. |
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BMV Quantum Subliminal CD Remember Names and Faces: Improve Your Memory for Remembering People (Ultrasonic Subliminal Series) Program your subconscious mind to remember faces and names so you can easily remember people by face. Create life-changing results using state-of-the-art subliminal and brainwave entrainment technologies. Tune your brainwaves to specific frequencies by listening to this CD! Program your subconscious mind for positive lasting results, created by a Certified Hypnotherapist and NLP Practitioner (Neuro-Linguistic Programming). Silent affirmations, inaudible hypnotic suggestions and thousands of powerful subliminal messages program your subconscious mind for positive results. The first 3 tracks have an ocean background. The Silent Ultrasonic Track 4 is completely silent with no sound at all! BMV exclusive Quantum Subliminal Matrix Technology sets a new standard for the subliminal industry! BMV... |
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VeriLook Standard SDK by Neurotechnology VeriLook SDK is based on the VeriLook PC-based face recognition technology and is intended for biometric systems developers and integrators. It allows rapid development of biometric applications using functions from the VeriLook library, which ensure high reliability of the face identification. VeriLook SDK enables 1:1 and 1:N matching modes, simultaneous multiple face detection, processing and identification with a comparison speed of 100,000 faces per second. |
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