Deep transfer across domains for face antispoofing

A practical face recognition system demands not only high recognition performance but also the capability of detecting spoofing attacks. While emerging approaches to face antispoofing have been proposed in recent years, most of them perform poorly on unseen samples. The generalizability of face antispoofing needs to be significantly improved before it can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.g., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials makes the spoofing attacks quite different. Another reason for the poor generalizability is that limited labeled data are available for training for face antispoofing. We focus on improving the generalizability of convolutional neural network (CNN)-based face antispoofing methods across different kinds of datasets. We propose a deep domain transfer CNN using sparsely unlabeled data from the target domain to learn features that are invariant across domains for face antispoofing. Experiments on five face spoofing datasets show that the proposed method significantly improves the cross-test performance only with a small number of unlabeled samples from the target domain.