Scene-Independent Feature Representation for Face Anti-Spoofing

Face anti-spoofing, as a security measure for face verification and recognition system, could distinguish between genuine and fake faces. Although some impressive results have been achieved by CNN when evaluated on intra-tests (i.e. the model is trained and tested on the same dataset). Unfortunately, most of these models fail to generalize well to unseen attacks (e.g. when the model is trained on one dataset and then evaluated on another dataset). This is a major concern in face anti-spoofing research which is mostly overlooked. Our experiments on two challenging benchmark face spoofing datasets, CASIA and Replay-Attack, demonstrate the disappointed adaptation ability for CNN from one dataset to another. Via visualizing the implicit attention of CNN, we find that scene-dependent features extracted by CNN affect models generalization capabilities. To address this problem, we propose a novel solution based on applying scene-independent features representation.