Verification of personal identity using facial images

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Abstract
A facial-imaging system to verify a person's supplied identity as part of a secure access control system is outlined. Classical image processing techniques transform the live-scan image to a standard position, scale, and lighting level. Two neural network classifiers, trained in a previous enrollment session, make the access decision. One neural net classifies the grayscale image directly. The other network uses as features the live-scan image's projection onto a general face-space similar to the approach of Turk and Pentland. This paper develops a method to generate additional dimensions, peculiar to the enrolled user, to augment the general face-space. This enhanced face-space enables the network to verify a specific person. A system with 16 enrolled users was attacked by 40 imposters with a false acceptance rate of 0.2%.

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