A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample

3D face recognition with the availability of only partial data (missing parts, occlusions and data corruptions) and single training sample is a highly challenging task. This paper presents an efficient 3D face recognition approach to address this challenge. We represent a facial scan with a set of local Keypoint-based Multiple Triangle Statistics (KMTS), which is robust to partial facial data, large facial expressions and pose variations. To address the single sample problem, we then propose a Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework. A class-based probability estimation is first calculated based on the extracted local descriptors as a prior knowledge. The resulting class-based probability estimation is then incorporated into the proposed classification framework as a locality constraint to further enhance its discriminating power. Experimental results on six challenging 3D facial datasets show that the proposed KMTS-TPWCRC framework achieves promising results for human face recognition with missing parts, occlusions, data corruptions, expressions and pose variations. HighlightsNovel Keypoint-based Multiple Triangle Statistics (KMTS) are proposed for 3D face representation.The proposed local descriptor is robust to partial facial data and expression/pose variations.A Two-Phase Weighted Collaborative Representation Classification (TPWCRC) framework is used to perform face recognition.The proposed classification framework can effectively address the single sample problem.State-of-the-art performance on six challenging datasets with high efficiency is achieved.