Collaborative representation with reduced residual for face recognition
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- 作者：Chang Yang (1)
Chengyin Liu (2)
Ning Wu (2)
Xiang Wu (3)
Yidong Li (4)
Zhiying Wang (2) - 关键词：Computer vision ; Face recognition ; Pattern recognition ; Sparse representation ; Transform methods
- 刊名：Neural Computing & Applications
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：25
- 期：7-8
- 页码：1741-1754
- 全文大小：1,238 KB
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Xiang Wu (3)
Yidong Li (4)
Zhiying Wang (2)
1. College of Economics, Shenzhen University, Shenzhen, 518060, China
2. Harbin Institute of Technology Shenzhen Graduate School, University Town, Shenzhen, 518055, China
3. Harbin Institute of Technology, Dazhi Street, Nangang District, Harbin, 150001, China
4. School of Computer and Information Technology, Beijing Jiaotong University, 3 Shangyuan Village, Beijing, 100044, China - ISSN：1433-3058
文摘
Collaboration representation-based classification (CRC) was proposed as an alternative approach to the sparse representation method with similar efficiency. The CRC is essentially a competition scheme for the training samples to compete with each other in representing the test sample, and the training class with the minimum representation residual from the test sample wins the competition in the classification. However, the representation error is usually calculated based on the Euclidean distance between a test sample and the weighted sum of all the same-class samples. This paper exploits alternative methods of calculating the representation error in the CRC methods to reduce the representation residual in a more optimal way, so that the sample classes compete with each other in a closer range to represent the test sample. A large number of face recognition experiments on three face image databases show that the CRC methods with optimized presentation residual achieve better performance than the original CRC, and the maximum improvement in classification accuracy is up to 12?%.