A Face Anti-spoofing Algorithm Based on Anomaly Detection in Disentangling Space

‍ ‍Most of the existing methods based on anomaly detection only used live samples for one-class modeling， and such features had strong generalization ability for face anti-spoofing but low accuracy. Moreover， the one-class modeling of live face features did not consider the diversity of live face samples. The different identities， environments， collection equipment and other factors of live face samples led to the incompact representation of live face features， which makes the features of spoof samples easily mixed into them. In order to solve the above two problems， we proposed a face anti-spoofing algorithm based on anomaly detection in disentangling space. In this paper， a single-center contrast loss is designed to make the representation of live face features more compact without restricting the distribution of spoof features. We also disentangled the features of the live face and divide its features into two subspaces： the anti-spoofing feature space and the liveness irrelevant feature space. The anti-spoofing feature space was not affected by other irrelevant factors， and the single-center contrast loss was combined to improve the generalization ability of the model. The proposed method was compared with state-of-the-art methods in intra-database experiments and cross-database experiments on a total of 5 datasets. In the OULU-NPU dataset， the error rate of protocol 1 dropped by more than half compared to the model with the second performance， and the error rate of the most challenging protocol 4 achieved 3.3%. It also achieved a lower error detection rate among the three protocols in the SiW dataset. In the cross-database experiments， the algorithm in this paper also showed good generalization ability. Especially in the cross-attack type test from replay attack and print attack to 3D mask attack， the error rate dropped by 5.41% compared to the second-performing model. The face anti-spoofing algorithm proposed in this paper is superior to other methods in detection performance and generalization performance， and the ability to deal with unknown data and new attack types has been improved.