Sample-Cohesive Pose-Aware Contrastive Facial Representation Learning

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 help calibrate them to appropriate positive pairs. Lastly, we devise two calibrated CL losses, namely calibrated pose-aware and face-aware CL losses, for adaptively learning the calibrated pairs more effectively, ultimately enhancing the learning with the disentangled features and providing robust facial representations for various downstream tasks. In the experiments, we perform linear evaluations on four challenging downstream facial tasks with SFRL using our method, including facial expression recognition, face recognition, facial action unit detection, and head pose estimation. Experimental results show that PCFRL outperforms existing state-of-the-art methods by a substantial margin, demonstrating the importance of improving pose awareness for SFRL. Our evaluation code and model will be available at https://github.com/fulaoze/CV/tree/main.