采用超复数小波生成对抗网络的活体人脸检测算法

Aiming at the problem that the existing anti-spoofing detection algorithms difficultly detect unknown domain attacks, this paper proposes a face anti-spoofing detection algorithm using generative adversarial networks with hypercomplex wavelet transform to improve the ability of face recognition system to determine the face anti-spoofing. Four different types of datasets are adopted, in which three datasets are randomly selected as the training ones, and the remainder as the test dataset to serve for unknown face anti-spoofing detection during training. The training datasets are regarded as three source domain datasets, which are input into the generation network to make a feature generator against three discriminators. When this feature generator successfully deceives the three discriminators, a feature space sharing three source domains and being different from these domains is formed to detect the characteristics of the unknown domain data. The triple constraint functions inter-class and intra-class are set up to improve the performance of the discriminator, and the detailed subbands of the hypercomplex wavelet transform and the convolution network are combined to learn the detailed features in multiple directions of images. Then the depth map and remote photo plethysmography signal are embedded in the feature space to enhance the generalization performance of the generated feature space for the living face features. The test dataset for discriminative classification in the feature space is used to obtain live/fake results. The results show that on the CASIA-FASD, Replay-Attack and NUAA datasets, the proposed algorithm gets the AUC of 84.65%, 86.06% and 91.21%, the HTER of 24.05%, 21.05% and 15.01%, which are higher than those of the comparative algorithms.