Face anti-spoofing detection based on DWT-LBP-DCT features

In this paper, we propose a face anti-spoofing strategy by using DWT (Discrete Wavelet Transform), LBP (Local Binary Pattern) and DCT (Discrete Cosine Transform) with a SVM classifier to evaluate whether a video is valid. Firstly, the DWT features are produced by decomposing some selected frames into different frequency components at the 8 ∗ 8 multi-resolution blocks. Secondly, the DWT-LBP features are generated to represent spatial information of the blocks by connecting LBP histograms of the DWT blocks in each frame horizontally. Then, the DWT-LBP-DCT features with the temporal information of a video file are achieved by performing DCT operation on the DWT-LBP features of those selected frames vertically. As a result, these exploited DWT-LBP-DCT features have the capacity to represent the frequency-spatial–temporal information of a video. Finally, the SVM classifier with RBF kernel is trained for face anti-spoofing. Compared with previous excellent works, experimental results on two benchmark databases (REPLAY-ATTACK and CASIA-FASD) have demonstrated the proposed approach has better detection performance.