Investigation in Spatial-Temporal Domain for Face Spoof Detection

Zhonglin Sun, Li Sun, Qingli Li

Abstract: This paper focuses on face spoofing detection using video. The purpose is to find out the best scheme for this task in the end-to-end learning manner. We investigate 4 different types of structure to fully exploit the raw data in its spatial-temporal domain, which are the pure CNN, CNN with 3D convolution, CNN+LSTM and CNN+Conv-LSTM. Moreover, another stream built on optical flow is also used, and with a proper fusion method, it can improve the accuracy. In experiments, we compare schemes on the raw data in single stream and fusion methods with optical flow in two streams. The performance are not only given within each dataset, but also measured across different datsets, which is crucial to avoid the overfitting.