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linear collaborative discriminant regression classification for face recognition

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2015年 31卷 312-319页

作者： Qu, Xiaochao Kim, Suah Cui, Run Kim, Hyoung Joong Korea Univ Grad Sch Informat Secur & Management Seoul 136713 South Korea

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. (C) 2015 Elsevier Inc. All rights reserved.

关键词： 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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linear discriminant regression classification for Face Recognition

引用

IEEE SIGNAL PROCESSING LETTERS 2013年第1期20卷 91-94页

作者： Huang, Shih-Ming Yang, Jar-Ferr Natl Cheng Kung Univ Inst Comp & Commun Engn Dept Elect Engn Tainan 70101 Taiwan

To improve the robustness of the linear regression classification (LRC) algorithm, in this paper, we propose a linear discriminant regression classification (LDRC) algorithm to boost the effectiveness of the LRC for face recognition. We embed the Fisher criterion into the LRC as a novel discriminant regression analysis method. The LDRC attempts to maximize the ratio of the between-class reconstruction error (BCRE) over the within-class reconstruction error (WCRE) to find an optimal projection matrix for the LRC such that the LRC on that subspace can achieve a high discrimination for classification. Then, the projected coefficients are executed by the LRC for face recognition. Extensive experiments carried out on the FERET and AR face databases show that the LDRC performs better than the related regression based algorithms and shows a promising ability for face recognition.

关键词： Face recognition linear discriminant regression classification linear regression classification

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