Linear collaborative discriminant regression classification for face recognition
- Authors
- Qu, Xiaochao; Kim, Suah; Cui, Run; Kim, Hyoung Joong
- Issue Date
- 8월-2015
- Publisher
- ACADEMIC PRESS INC ELSEVIER SCIENCE
- Keywords
- Face recognition; Feature extraction; Dimensionality reduction; Collaborative representation; Sparse representation; Linear regression classification; Linear collaborative discriminant regression classification; Linear discriminant regression classification
- Citation
- JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, v.31, pp.312 - 319
- Indexed
- SCIE
SCOPUS
- Journal Title
- JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
- Volume
- 31
- Start Page
- 312
- End Page
- 319
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/92860
- DOI
- 10.1016/j.jvcir.2015.07.009
- ISSN
- 1047-3203
- 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. (C) 2015 Elsevier Inc. All rights reserved.
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