Adversarial Unsupervised Domain Adaptation Guided with Deep Clustering for Face Presentation Attack Detection

Yomna Safaa El-Din, Mohamed N. Moustafa, Hani Mahdi

Abstract: Face Presentation Attack Detection (PAD) has drawn increasing attentions to secure the face recognition systems that are widely used in many applications. Conventional face anti-spoofing methods have been proposed, assuming that testing is from the same domain used for training, and so cannot generalize well on unseen attack scenarios. The trained models tend to overfit to the acquisition sensors and attack types available in the training data. In light of this, we propose an end-to-end learning framework based on Domain Adaptation (DA) to improve PAD generalization capability. Labeled source-domain samples are used to train the feature extractor and classifier via cross-entropy loss, while unsupervised data from the target domain are utilized in adversarial DA approach causing the model to learn domain-invariant features. Using DA alone in face PAD fails to adapt well to target domain that is acquired in different conditions with different devices and attack types than the source do