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

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 rec...

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Bibliographic Details
Published in:Tsinghua science and technology 2013-02, Vol.18 (1), p.62-67
Main Authors: Wang, Jing, Su, Guangda, Xiong, Ying, Chen, Jiansheng, Shang, Yan, Liu, Jiongxin, Ren, Xiaolong
Format: Article
Language:English
Subjects:
SRC
人脸识别
提取方法
稀疏表示
识别精度
识别问题
配准
采样
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Summary: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%）.
ISSN:1007-0214
1878-7606
1007-0214
DOI:10.1109/TST.2013.6449409