Deep Tree Learning for Zero-shot Face Anti-Spoofing

Face anti-spoofing is designed to prevent face recognition systems from recognizing fake faces as the genuine users. While advanced face anti-spoofing methods are developed, new types of spoof attacks are also being created and becoming a threat to all existing systems. We define the detection of unknown spoof attacks as Zero-Shot Face Anti-spoofing (ZSFA). Previous ZSFA works only study 12 types of spoof attacks, such as print/replay, which limits the insight of this problem. In this work, we investigate the ZSFA problem in a wide range of 13 types of spoof attacks, including print, replay, 3D mask, and so on. A novel Deep Tree Network (DTN) is proposed to partition the spoof samples into semantic sub-groups in an unsupervised fashion. When a data sample arrives, being know or unknown attacks, DTN routes it to the most similar spoof cluster, and makes the binary decision. In addition, to enable the study of ZSFA, we introduce the first face anti-spoofing database that contains diverse types of spoof attacks. Experiments show that our proposed method achieves the state of the art on multiple testing protocols of ZSFA.