Face anti-spoofing algorithm combined with CNN and brightness equalization

Abstract

Face anti-spoofing is a relatively important part of the face recognition system, which has great significance for financial payment and access control systems. Aiming at the problems of unstable face alignment, complex lighting, and complex structure of face anti-spoofing detection network, a novel method is presented using a combination of convolutional neural network and brightness equalization. Firstly, multi-task convolutional neural network (MTCNN) based on the cascade of three convolutional neural networks (CNNs), P-net, R-net, and O-net are used to achieve accurate positioning of the face, and the detected face bounding box is cropped by a specified multiple, then brightness equalization is adopted to perform brightness compensation on different brightness areas of the face image. Finally, data features are extracted and classification is given by utilizing a 12-layer convolution neural network. Experiments of the proposed algorithm were carried out on CASIA-FASD. The results show that the classification accuracy is relatively high, and the half total error rate (HTER) reaches 1.02%.