3D Face recognition using distinctiveness enhanced facial representations and local feature hybrid matching

This paper presents a simple yet effective approach for 3D face recognition. A novel 3D facial surface representation, namely Multi-Scale Local Binary Pattern (MS-LBP) Depth Map, is proposed, which is used along with the Shape Index (SI) Map to increase the distinctiveness of smooth range faces. Scale Invariant Feature Transform (SIFT) is introduced to extract local features to enhance the robustness to pose variations. Moreover, a hybrid matching is designed for a further improved accuracy. The matching scheme combines local and holistic analysis. The former is achieved by comparing the SIFT-based features extracted from both 3D facial surface representations; while the latter performs a global constraint using facial component and configuration. Compared with the state-of-the-art, the proposed method does not require time-consuming accurate registration or any additional data in a bootstrap for training special thresholds. The rank-one recognition rate achieved on the complete FRGC v2.0 database is 96.1%. As a result of using local facial features, the approach proves to be competent for dealing with partially occluded face probes as highlighted by supplementary experiments using face masks.