Camera Invariant Feature Learning for Generalized Face Anti-spoofing
This addresses the challenge of generalizing face spoofing detection across different acquisition devices, which is crucial for real-world security applications, though it appears incremental as it builds on existing feature learning methods.
The paper tackles the problem of domain gaps in face anti-spoofing caused by camera model divergence by proposing a framework that learns camera-invariant features through high-frequency decomposition and feature enhancement, achieving better performance in intra-dataset and cross-dataset settings.
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.