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Combination of linear regression classification and collaborative representation classification |
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| Authors: | Hongzhi Zhang Faqiang Wang Yan Chen Dapeng Zhang Kuanquan Wang Jingdong Liu |
| Affiliation: | 1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China 2. College of Information and Computer Engineering, Northeast Forestry University, Harbin, China 3. Harbin Vicog Intelligent Systems Co., Ltd, Harbin, China |
| Abstract: | Classification using the l 2-norm-based representation is usually computationally efficient and is able to obtain high accuracy in the recognition of faces. Among l 2-norm-based representation methods, linear regression classification (LRC) and collaborative representation classification (CRC) have been widely used. LRC and CRC produce residuals in very different ways, but they both use residuals to perform classification. Therefore, by combining the residuals of these two methods, better performance for face recognition can be achieved. In this paper, a simple weighted sum based fusion scheme is proposed to integrate LRC and CRC for more accurate recognition of faces. The rationale of the proposed method is analyzed. Face recognition experiments illustrate that the proposed method outperforms LRC and CRC. |
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