Sample-Cohesive Pose-Aware Contrastive Facial Representation Learning

作者：Yuanyuan Liu, Shaoze Feng, Shuyang Liu, Yibing Zhan, Dapeng Tao, Zijing Chen, Zhe Chen, Zhe Chen, Zhe Chen · 发表于：International Journal of Computer Vision · 年份：2025 · DOI：10.1007/s11263-025-02348-z · 被引用次数：28 · 研究领域：Face recognition and analysis、Facial Nerve Paralysis Treatment and Research、Face and Expression Recognition

Abstract Self-supervised facial representation learning (SFRL) methods, especially contrastive learning (CL) methods, have been increasingly popular due to their ability to perform face understanding without heavily relying on large-scale well-annotated datasets. However, analytically, current CL-based SFRL methods still perform unsatisfactorily in learning facial representations due to their tendency to learn pose-insensitive features, resulting in the loss of some useful pose details. This could be due to the inappropriate positive/negative pair selection within CL. To conquer this challenge, we propose a Pose-disentangled Contrastive Facial Representation Learning (PCFRL) framework to enhance pose awareness for SFRL. We achieve this by explicitly disentangling the pose-aware features from non-pose face-aware features and introducing appropriate sample calibration schemes for better CL with the disentangled features. In PCFRL, we first devise a pose-disentangled decoder with a delicately designed orthogonalizing regulation to perform the disentanglement; therefore, the learning on the pose-aware and non-pose face-aware features would not affect each other. Then, we introduce a false-negative pair calibration module to overcome the issue that the two types of disentangled features may not share the same negative pairs for CL. Our calibration employs a novel neighborhood-cohesive pair alignment method to identify pose and face false-negative pairs, respectively, and further h...