Cross-domain Face Presentation Attack Detection via Multi-domain Disentangled Representation Learning
This addresses a critical security issue in face recognition systems by enhancing generalization to unseen attack scenarios, though it is incremental as it builds on existing disentanglement techniques.
The paper tackles the problem of face presentation attack detection (PAD) generalizing poorly across domains by proposing a multi-domain disentangled representation learning method, achieving improved cross-domain performance validated on public datasets.
Face presentation attack detection (PAD) has been an urgent problem to be solved in the face recognition systems. Conventional approaches usually assume the testing and training are within the same domain; as a result, they may not generalize well into unseen scenarios because the representations learned for PAD may overfit to the subjects in the training set. In light of this, we propose an efficient disentangled representation learning for cross-domain face PAD. Our approach consists of disentangled representation learning (DR-Net) and multi-domain learning (MD-Net). DR-Net learns a pair of encoders via generative models that can disentangle PAD informative features from subject discriminative features. The disentangled features from different domains are fed to MD-Net which learns domain-independent features for the final cross-domain face PAD task. Extensive experiments on several public datasets validate the effectiveness of the proposed approach for cross-domain PAD.