Improving Cross-database Face Presentation Attack Detection via Adversarial Domain Adaptation

Face recognition (FR) is being widely used in many applications from access control to smartphone unlock. As a result, face presentation attack detection (PAD) has drawn increasing attentions to secure the FR systems. Traditional approaches for PAD mainly assume that training and testing scenarios are similar in imaging conditions (illumination, scene, camera sensor, etc.), and thus may lack good generalization capability into new application scenarios. In this work, we propose an end-to-end learning approach to improve PAD generalization capability by utilizing prior knowledge from source domain via adversarial domain adaptation. We first build a source domain PAD model optimized with triplet loss. Subsequently, we perform adversarial domain adaptation w.r.t. the target domain to learn a shared embedding space by both the source and target domain models, in which the discriminator cannot reliably predict whether a sample is from the source or target domain. Finally, PAD in the target domain is performed with k-nearest neighbors (k-NN) classifier in the embedding space. The proposed approach shows promising generalization capability in a number of public-domain face PAD databases.