An efficient 3D face recognition approach using local geometrical signatures

作者：

Highlights：

• Novel facial Angular Radial Signatures (ARSs) are proposed for 3D face recognition.

• The Signatures are extracted from the semi-rigid facial regions.

• A two-stage mapping-based classification strategy is used to perform face recognition.

• ARSs combined with machine learning techniques can handle expression variations.

• State-of-the-art performance on two public datasets with high efficiency is achieved.

摘要

Highlights•Novel facial Angular Radial Signatures (ARSs) are proposed for 3D face recognition.•The Signatures are extracted from the semi-rigid facial regions.•A two-stage mapping-based classification strategy is used to perform face recognition.•ARSs combined with machine learning techniques can handle expression variations.•State-of-the-art performance on two public datasets with high efficiency is achieved.

论文关键词：3D biometrics,3D face recognition,3D representation,KPCA,SVM

论文评审过程：Received 26 March 2013, Revised 19 June 2013, Accepted 26 July 2013, Available online 7 August 2013.

论文官网地址：https://doi.org/10.1016/j.patcog.2013.07.018