There has been an increasing consensus in learning based face anti-spoofing that the divergence in terms of camera models is causing a large domain gap in real application scenarios. We describe a framework that eliminates the influence of inherent variance from acquisition cameras at the feature level, leading to the generalized face spoofing detection model that could be highly adaptive to different acquisition devices. In particular, the framework is composed of two branches. The first branch aims to learn the camera invariant spoofing features via feature level decomposition in the high frequency domain. Motivated by the fact that the spoofing features exist not only in the high frequency domain, in the second branch the discrimination capability of extracted spoofing features is further boosted from the enhanced image based on the recomposition of the high-frequency and low-frequency information. Finally, the classification results of the two branches are fused together by a weighting strategy. Experiments show that the proposed method can achieve better performance in both intra-dataset and cross-dataset settings, demonstrating the high generalization capability in various application scenarios.
|Journal||IEEE Transactions on Information Forensics and Security|
|Early online date||27 Jan 2021|
|Publication status||Published - 2021|
Bibliographical noteThis work was supported in part by the Science, Technology, and Innovation Commission of Shenzhen Municipality under Project JCYJ20180307123934031, in part by the National Natural Science Foundation of China under Grant 62022002, in part by the Hong Kong Research Grants Council, Early Career Scheme (RGC ECS) under Grant 21211018, and in part by the General Research Fund (GRF) under Grant 11203220.
- camera invariant
- deep learning
- Face anti-spoofing
- generalization capability