Point cloud feature consistency learning for incomplete 3D face recognition

Abstract

Point cloud-based 3D face recognition has emerged as an exciting research topic due to the availability of 3D facial structures and detailed surface information. Existing approaches have primarily focused on complete facial point clouds and have achieved remarkable results. However, in real-world applications, the collected facial point clouds are often incomplete due to factors such as various poses, occlusion, and noise, posing significant challenges to face recognition tasks. In this paper, a feature consistency learning framework is proposed to improve incomplete 3D face recognition. The feature gap between incomplete and complete data is filled through joint optimization of completion and supervised contrastive learning. Specifically, to maintain and enhance the structure of incomplete point clouds, we introduce a structure-enhanced representation method for neighboring points that incorporates positional information residuals during the formation of point proxies. Additionally, a simple and effective dynamic input approach within the point proxy completion process is designed to alleviate concerns related to density disparities and detail loss in point clouds that exhibit relatively minor degrees of incompleteness. Extensive experiments on four datasets demonstrate our proposed method outperforms state-of-the-art methods on both inherent and artificially constructed incomplete data. Moreover, it also achieves comparable results on complete 3D face recognition. Overall, this work represents an early exploration into the realm of point cloud-based incomplete 3D face recognition through feature consistency learning, providing a promising approach for practical applications.