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The Shallow Network with Learnable Attention Module For Face Anti-spoofing

With the development of face recognition systems in mobile devices and intelligent systems, the vulnerability of face recognition techniques have been widely focused as it will introduce tremendous risks for these systems. So the face anti-spoofing approaches are developed to avoid the influence of fake faces by printed or replayed ways. The existing methods are devoted to improve the performance of face anti-spoofing while the runtime of detecting fake faces in a fast speed is also important in real-world applications. Therefore, in this paper, we propose a shallow network to speed up the runtime of deep model while reserving the performance of predicting fake faces. In the shallow network, one convolution layer and two recurrent convolution layer are used to extract the visual features for the fake faces. Then a spatial-channel attention layer is used to follow the important regions and feature maps in the network. The most challenging dataset, namely SiW dataset, is used to evaluate our proposed method and the results are proven the efficiency and effectiveness of the proposed method.