Face Anti-Spoofing Via Disentangled Representation Learning
This addresses security vulnerabilities in face recognition systems by improving generalization against spoofing attacks, though it appears incremental as it builds on existing disentanglement methods.
The paper tackles face anti-spoofing by proposing a disentangled representation learning approach that separates liveness and content features from images, using the liveness features for classification, and demonstrates effectiveness with state-of-the-art results on public benchmarks.
Face anti-spoofing is crucial to security of face recognition systems. Previous approaches focus on developing discriminative models based on the features extracted from images, which may be still entangled between spoof patterns and real persons. In this paper, motivated by the disentangled representation learning, we propose a novel perspective of face anti-spoofing that disentangles the liveness features and content features from images, and the liveness features is further used for classification. We also put forward a Convolutional Neural Network (CNN) architecture with the process of disentanglement and combination of low-level and high-level supervision to improve the generalization capabilities. We evaluate our method on public benchmark datasets and extensive experimental results demonstrate the effectiveness of our method against the state-of-the-art competitors. Finally, we further visualize some results to help understand the effect and advantage of disentanglement.