Author/Authors :
Wang, Jing Tsinghua University - Department of Electronic Engineering, China , Su, Guangda Tsinghua University - Department of Electronic Engineering, China , Xiong, Ying Harvard University - Department of Engineering and Applied Sciences, USA , Chen, Jiansheng Tsinghua University - Department of Electronic Engineering, China , Shang, Yan Tsinghua University - Department of Electronic Engineering, China , Liu, Jiongxin Columbia University - Department of Computer Science, USA , Ren, Xiaolong Tsinghua University - Department of Electronic Engineering, China
Abstract :
Sparse Representation based Classification (SRC) has emerged as a new paradigm for solvingrecognition problems. This paper presents a constraint sampling feature extraction method that improves the SRC recognition rate. The method combines texture and shape features to significantly improve the recognition rate. Tests show that the combined constraint sampling and facial alignment achieves very high recognition accuracy on both the AR face database (99.52%) and the CAS-PEAL face database (99.54%).
Keywords :
classification , face recognition , feature extraction , face alignment
