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Open Access

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

Jing WangGuangda Su( )Ying XiongJiansheng ChenYan ShangJiongxin LiuXiaolong Ren
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
Department of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Department of Computer Science, Columbia University, New York, NY 10027, USA
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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%).


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Tsinghua Science and Technology
Pages 62-67
Cite this article:
Wang J, Su G, Xiong Y, et al. Sparse Representation for Face Recognition Based on Constraint Sampling and Face Alignment. Tsinghua Science and Technology, 2013, 18(1): 62-67.








Web of Science






Received: 16 July 2012
Accepted: 05 December 2012
Published: 07 February 2013
© The author(s) 2013