Face presentation attack detection based on optical flow and texture analysis

Towards the security threats brought by presented fake faces to face recognition systems, many countermeasures have been presented to resist fake faces and achieved promising performance, where facial movement is one of the commonly used cues. However, the more detailed and distinguishable information in the motion cue is not well explored in these methods. In addition, the texture cue used for face presentation attack detection (PAD) is also not well integrated into motion. Therefore, we propose a detection method by analyzing the cues of facial movement and texture. More specifically, the optical flows of a continuous video sequence are first extracted, which can describe the detailed movement direction and movement amplitude. Then, the extracted optical flows are concatenated with the video frames as the input of the network. After that, region and channel attention mechanisms are jointly introduced to adaptively allocate the classification weights. Finally, the fused motion and texture cues are fed into a convolutional network to extract features and identify whether the input video sequence is from live face or not. The proposed detection method is tested on the databases of Replay-Attack, OULU-NPU and HKBU-MARs V1. The experiments show that our proposed face PAD method can well separate various types of fake faces compared to state-of-the-art methods.