shallowCNN-LE: A shallow CNN with Laplacian Embedding for face anti-spoofing

Identity authentication based on face recognition has been significantly improved due to the outstanding ability of face detection, thus it plays an important role in society. However, face recognition system might be deceived by malicious face spoof attacks raising risk from both safety and property. The algorithm to accurately detect face anti-spoofing in identity authentication system is becoming crucial. In this paper, a shallow convolutional neural network with laplacian embedding (shallowCNN-LE) is proposed for face anti-spoofing. Two different types of features are concatenated to accurately detect the face liveness, including depth features and dynamic texture features. First, the developed shallow CNN model contains four layers which make the model faster. Second, we integrate dynamic texture features extracted by using the dual tree complex wavelet transform (DT-CWT) with the depth features as input features to feed into the proposed model. Finally, we propose a laplacian embedding algorithm, which can maintain the inter-class discrimination and penalize the distance of intra-class. When embedding the laplacian loss with the softmax loss, the proposed method can obtain much more discriminative features, which is helpful to detect face anti-spoofing. Experimental results on public databases of CASIA FASD, Replay attack and MSU USSA database demonstrate that our proposed method outperforms the state-of-the-art methods for face anti-spoofing detection.