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Face image biometric verification algorithm based on deep convolution neural network


V.V. Khryashchev – Ph. D. (Eng.), Associate Professor of Department of Infocommunication and Radiophysics, P.G. Demidov Yaroslavl State University
A.A. Lebedev – Master of Department of Infocommunication and Radiophysics, P.G. Demidov Yaroslavl State University
A.M. Shemyakov – Post-graduate Student of Department of Infocommunication and Radiophysics, P.G. Demidov Yaroslavl State University
V.A. Pavlov – Post-graduate Student of Department of Infocommunication and Radiophysics, P.G. Demidov Yaroslavl State University

This paper presents the new algorithm for face recognition based on deep convolution neural network. The algorithm produces face feature vectors, distance between these vectors allows to determine whether images from the same class. Comparative experimental results are given for LFW test database and modern face recognition algorithms. ROC-curve and EER are used to determine the accuracy of compared algorithms.
Testing was carried out under the «image resctricted» verification paradigm. With uncontrolled learning, the algorithm can’t have any access to the data class labels, the statistics of these labels, or the means of generating these labels. Proposed face recognition algorithm is more accurate than other modern algorithms.

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