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

作者：Lei, Yinjie; Guo, Yulan; Hayat, Munawar; Bennamoun, Mohammed; Zhou, Xinzhi^*

来源：Pattern Recognition, 2016, 52: 218-237.

DOI：10.1016/j.patcog.2015.09.035

• 3D face recognition
• 3D representation
• Sparse representation
• Partial facial data
• Single sample problem

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.

• 出版日期2016-4
• 单位中国人民解放军国防科学技术大学; 四川大学; IBM