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Found 7 papers, 2 papers with code
FaceCat: Enhancing Face Recognition Security with a Unified Generative Model Framework
Motivated by the rich structural and detailed features of face generative models, we propose FaceCat which utilizes the face generative model as a pre-trained model to improve the performance of FAS and FAD.
Seeing through the Mask: Multi-task Generative Mask Decoupling Face Recognition
Therefore, this paper proposes a Multi-task gEnerative mask dEcoupling face Recognition (MEER) network to jointly handle these two tasks, which can learn occlusionirrelevant and identity-related representation while achieving unmasked face synthesis.
AdvFAS: A robust face anti-spoofing framework against adversarial examples
Ensuring the reliability of face recognition systems against presentation attacks necessitates the deployment of face anti-spoofing techniques.
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Experimental results show that VIAT significantly improves the viewpoint robustness of various image classifiers based on the diversity of adversarial viewpoints generated by GMVFool.
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object.
Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization
However, numerous studies have proven that previous methods create detection or defense against certain attacks, which renders the method ineffective in the face of the latest unknown attack methods.
A Survey of Face Recognition
Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks.
