"Sparse Representation for Face Recognition Based on Constraint Samplin" by Jing Wang, Guangda Su et al.

Article Title

Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment

Authors

Jing Wang, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Guangda Su, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Ying Xiong, Department of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Jiansheng Chen, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Yan Shang, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Jiongxin Liu, Department of Computer Science, Columbia University, New York, NY 10027, USA
Xiaolong Ren, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Keywords

classification, face recognition, feature extraction, face alignment

Abstract

Sparse Representation based Classification (SRC) has emerged as a new paradigm for solving recognition 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%).

DOI

https://doi.org/10.1109/TST.2013.6449409

Publisher

Tsinghua University Press

Recommended Citation

Jing Wang, Guangda Su, Ying Xiong et al. Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment. Tsinghua Science and Technology 2013, 18(1): 62-67.

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