Advanced Face Anti-Spoofing with Depth Segmentation

Face anti-spoofing (FAS) plays a vital role in securing face recognition systems. In state-of-the-art FAS methods, face depth is determined for every position in a facial image. However, face depth varies at different positions, which leads to low accuracy when predicting face depth. As we observe, spoof faces have depth values that are 0, while live faces have depths that are equal to or greater than 0. As a result, for faces that have a depth greater than 0, if they are estimated as merely a positive value, instead of an accurate value, the prediction of whether they are real or not will not change. Further, if a range of depths are considered as one category, then there are more samples per category for the network training. Based on the above observation, in this paper, we propose to aggregate simple depth values to the same category and perform classification to optimize the FAS network. To evaluate the performance of the proposed approach, we perform extensive experiments on four benchmark databases, respectively, OULU-NPU, SiW, CASIA-FASD, and Replay-Attack. The results demonstrate that the proposed approach outperforms state-of-the-art methods on intra-database testing. Furthermore, our proposed approach shows advanced performance on cross-database testing.