23-05-2014, 03:06 PM
FACE RECOGNITION (Pattern Matching And Bio Metrics)
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ABSTRACT:
Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weaknesses in today’s safety mechanisms. Badge or password-based authentication procedures are too easy to hack. Biometrics represents a valid alternative but they suffer of drawbacks as well. Iris scanning, for example, is very reliable but too intrusive; fingerprints are socially accepted, but not applicable to non-consentient people. On the other hand, face recognition represents a good compromise between what’s socially acceptable and what’s reliable, even when operating under controlled conditions. In last decade, many algorithms based on linear/nonlinear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, Face Recognition Vendor Test 2002 shown that most of these approaches encountered problems in outdoor conditions. This lowered their reliability compared to state of the art biometrics.
What is Face Recognition?
Face recognition technology is the least intrusive and fastest biometric technology. It works with the most obvious individual identifier –the human face.
Instead of requiring people to place their hand on a reader or precisely position their eye in front of a scanner, face recognition systems unobtrusively take pictures of people's faces as they enter a defined area. There is no intrusion or delay, and in most cases the subjects are entirely unaware of the process. They do not feel "under surveillance" or that their privacy has been invaded.
TECHNOLOGY
Our technology is based on neural computing and combines the advantages of elastic and neural networks.
Neural computing provides technical information processing methods that are similar to the way information is processed in biological systems, such as the human brain. They share some key strengths, like robustness fault-resistance and the ability to learn from examples. Elastic networks can compare facial landmarks even if images are not identical, as is practically always the case in real-world situations. Neural networks can learn to recognize similarities through pattern recognition.
Verification or Identification
In verification, an image is matched to only one image in the database (1:1). For example, an image taken of a subject may be matched to an image in the Department of Motor Vehicles database to verify the subject is who he says he is. If identification is the goal, then the image is compared to all images in the database resulting in a score for each potential match (1:N). In this instance, you may take an image and compare it to a database of mug shots to identify who the subject is.
Hard to fool
Face recognition is also very difficult to fool. It works by comparing facial landmarks - specific proportions and angles of defined facial features - which cannot easily be concealed by beards, eyeglasses or makeup.
However, there are now many more situations where the software is becoming popular.
As the systems become less expensive, making their use more widespread.
They are now compatible with cameras and computers that are already in use by banks and airports. Registered Traveler program will provide speedy security screening for passengers who volunteer information. At the airport there will be specific lines for the Registered Traveler to go through that will move more quickly, verifying the traveler by their facial features.
Other potential applications include ATM and check-cashing security. After a customer consents, the ATM or check-cashing kiosk captures a digital image of him. The FaceIt software then generates a face print of the photograph to protect customers against identity theft and fraudulent transactions.
DRAWBACKS OF THIS TECHNOLOGY
While all the examples above work with the permission of the individual, not all systems are used with your knowledge.
These systems were taking pictures of all visitors without their knowledge or their permission. Opponents of the systems note that while they do provide security in some instances, it is not enough to override a sense of liberty and freedom.
Many feel that privacy infringement is too great with the use of these systems, but their concerns don't end there.
They also point out the risk involved with identity theft. Even facial recognition corporations admit that the more use the technology gets, the higher the likelihood of identity theft or fraud.
CONCLUSION
As with many developing technologies, the incredible potential of facial recognition comes with some drawbacks, but manufacturers are striving to enhance the usability and accuracy of the systems. Face recognition promises latest security invents in the upcoming trends based on bio-metrics and pattern matching techniques and algorithms.