Linear collaborative discriminant regression classification for face recognition |
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| Affiliation: | 1. College of Engineering, Huazhong Agricultural University, Wuhan 430070, China;2. College of Informatics, Huazhong Agricultural University, Wuhan 430070, China;3. School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;4. Key Laboratory of Digital Earth, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;1. Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China;2. Department of Automation, Tsinghua University, Beijing, China;3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;4. Department of Electrical Engineering, Princeton University, NJ 08544, USA;1. Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310027, China;2. The State Key Lab of CAD&CG, Zhejiang University, Hangzhou 310027, China;1. Department of Computer Science and Engineering, Indian School of Mines, Dhanbad 826004, India;2. Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur 721302, India;1. Cloud Technology Lab, Software R&D Center, Samsung Electronics Co., Ltd., Republic of Korea;2. Dept. of IT Convergence Engineering, POSTECH (Pohang University of Science and Technology), Republic of Korea;3. Computer Science and Engineering, POSTECH (Pohang University of Science and Technology), Pohang 790-784, Republic of Korea |
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| Abstract: | This paper proposes a novel face recognition method that improves Huang’s linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified. |
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| Keywords: | Face recognition Feature extraction Dimensionality reduction Collaborative representation Sparse representation Linear regression classification Linear collaborative discriminant regression classification Linear discriminant regression classification |
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