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ACCESS CONTROL BY FACE RECOGNITION USING NEURAL NETWORKS*
Dmitry Bryliuk and Valery Starovoitov Institute of Engineering Cybernetics, Laboratory of Image Processing and Recognition Surganov str., 6, 220012 Minsk, BELARUS E-mail: [e-mail], [e-mail]
A Multilayer Perceptron Neural Network (NN) is considered for access control based on face image recognition. We studied robustness of NN classifiers with respect to the False Acceptance and False Rejection errors. A new thresholding approach for rejection of unauthorized persons is proposed. Ensembles of NN with different architectures were studied too. Advantages of the ensembles are shown, and the best architecture parameters are given. The explored NN architectures may be used in real-time applications.

Introduction Access control by face recognition has the following advantages in comparison with other biometrics systems. There are no requirements for expensive or specialized equipment, a system may be built using a simple video camera and a personal computer. The system is passive. There is no need to touch something by fingers or palm, no need to say any word or lean eye to a detector. Any person just may walk or stay before the camera, and the system performs recognition. It is especially useful in everyday usage. Also it has advantages in different extremal or non-standard situations, when it is impossible or inconvenient to took other biometric characteristics, for example when catching criminals. The recognition performance of a simple face recognition system is not the best in comparison with other biometric-based systems, and such a system can be relatively easy deceived. But using a face thermogram or output of an infrared camera, the system can achieve very high recognition rate and robustness to deceiving. The face thermogram is strictly individual for every person, it does not change when lighting condition are changed, and

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