Unseen Face Presentation Attack Detection With Hypersphere Loss

Presentation attack is one of the main threats to face verification systems and attracts great attention of research community. Recent methods achieve great success in intra-database test. However, the problem is more complex in practical scenario as the type of attack could be unseen to system designers. In this paper, we formulate the face presentation attack detection task under an open-set setting and address with our proposed deep anomaly detection based method. The training process is end-to-end supervised by a novel hypersphere loss function and the decision making is directly based on the learned feature representation. We conduct extensive experiments on multiple prevailing databases and evaluate our implemented models by using various metrics. The results show our proposed method is effective against unseen types of attacks and superior to latest state-of-the-art.