Abstract: Face liveness detection is a key factor in ensuring the security of face recognition systems. In particular, disentangled learning methods can effectively address the problem of generalizing datasets in face liveness detection. However, existing disentangled learning methods often take the entire face image as input and parse out forged trace elements, ignoring the issue of local details of forged traces. To address this issue, this paper improves the existing forgery trace disentanglement network and proposes a multi-adversarial discriminative network model. The discriminator is designed with a primary discriminator and a regional discriminator. A facial mask module is introduced to generate facial skin and feature masks. Local facial information is integrated to make the generated images more closely resemble the distribution of face images in the dataset, while also disentangling an enhanced version of the forgery trace. The proposed multi-adversarial discriminative network effectively enhances the effect of forgery trace on forged face images and improves the accuracy of face liveness detection. Specifically, the detection error rate of our model on the OULU-NPU dataset in two experiments is only 0.8% and 1.4%, significantly lower than that of the STDN. At the same time, good detection results are achieved on the Idiap Replay-Attack dataset. To verify the transferability of our method, cross-domain experiments on the NUAA dataset and the Idiap Replay-Attack dataset also achieves good results.

Key words: face recognition, liveness detection, generative adversarial network, disentangled representation learning