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The overview of our approach for cross-domain PAD. Our approach consists of a disentangled representation learning module (DR-Net) and a multi-domain feature learning module (MD-Net). With the face images from different domains as inputs, DR-Net can learn a pair of encoders for disentangled features for PAD and subject classification respectively. The disentangled features are fed to MD-Net to learn domain-independent representations for robust cross-domain PAD.
Source publication
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 t...
Contexts in source publication
Context 1
... aim to build a robust cross-domain face PAD model that can mitigate the impact of the subjects, shooting environment and camera settings from the source domain face images. To achieve this goal, we propose an efficient disentangled representation learning for cross-domain face PAD. As shown in Fig. 2, our approach consists of a disentangled representation learning module (DR-Net) and a multidomain feature learning module (MD-Net). DR-Net leverages generative models (i.e., PAD-GAN and ID-GAN in Fig. 2) to learn a pair of encoders for learning disentangled features for subject classification and PAD respectively in each source ...
Context 2
... domain face images. To achieve this goal, we propose an efficient disentangled representation learning for cross-domain face PAD. As shown in Fig. 2, our approach consists of a disentangled representation learning module (DR-Net) and a multidomain feature learning module (MD-Net). DR-Net leverages generative models (i.e., PAD-GAN and ID-GAN in Fig. 2) to learn a pair of encoders for learning disentangled features for subject classification and PAD respectively in each source domain. Then, the disentangled features from multiple domains are fed to MD-Net to learn domainindependent features for the final cross-domain face ...
Context 3
... aim to build a robust cross-domain face PAD model that can mitigate the impact of the subjects, shooting environment and camera settings from the source domain face images. To achieve this goal, we propose an efficient disentangled representation learning for cross-domain face PAD. As shown in Fig. 2, our approach consists of a disentangled representation learning module (DR-Net) and a multidomain feature learning module (MD-Net). DR-Net leverages generative models (i.e., PAD-GAN and ID-GAN in Fig. 2) to learn a pair of encoders for learning disentangled features for subject classification and PAD respectively in each source ...
Context 4
... domain face images. To achieve this goal, we propose an efficient disentangled representation learning for cross-domain face PAD. As shown in Fig. 2, our approach consists of a disentangled representation learning module (DR-Net) and a multidomain feature learning module (MD-Net). DR-Net leverages generative models (i.e., PAD-GAN and ID-GAN in Fig. 2) to learn a pair of encoders for learning disentangled features for subject classification and PAD respectively in each source domain. Then, the disentangled features from multiple domains are fed to MD-Net to learn domainindependent features for the final cross-domain face ...
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[This corrects the article DOI: 10.1371/journal.pone.0232551.].
