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

作者：

Highlights：

• Novel 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.

摘要

Highlights•Novel 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.

论文关键词：3D face recognition,3D representation,Sparse representation,Partial facial data,Single sample problem

论文评审过程：Received 25 March 2015, Revised 30 June 2015, Accepted 30 September 2015, Available online 23 October 2015, Version of Record 24 December 2015.

论文官网地址：https://doi.org/10.1016/j.patcog.2015.09.035