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.
| Original language | English |
|---|---|
| Pages (from-to) | 312-319 |
| Number of pages | 8 |
| Journal | Journal of Visual Communication and Image Representation |
| Volume | 31 |
| DOIs | |
| Publication status | Published - 2015 Jul 27 |
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Keywords
- Collaborative representation
- Dimensionality reduction
- Face recognition
- Feature extraction
- Linear collaborative discriminant regression classification
- Linear discriminant regression classification
- Linear regression classification
- Sparse representation
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Media Technology
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Linear collaborative discriminant regression classification for face recognition. / Qu, Xiaochao; Kim, Suah; Cui, Run; Kim, Hyong Joong.
In: Journal of Visual Communication and Image Representation, Vol. 31, 27.07.2015, p. 312-319.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Linear collaborative discriminant regression classification for face recognition
AU - Qu, Xiaochao
AU - Kim, Suah
AU - Cui, Run
AU - Kim, Hyong Joong
PY - 2015/7/27
Y1 - 2015/7/27
N2 - 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.
AB - 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.
KW - Collaborative representation
KW - Dimensionality reduction
KW - Face recognition
KW - Feature extraction
KW - Linear collaborative discriminant regression classification
KW - Linear discriminant regression classification
KW - Linear regression classification
KW - Sparse representation
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UR - http://www.scopus.com/inward/citedby.url?scp=84937808290&partnerID=8YFLogxK
U2 - 10.1016/j.jvcir.2015.07.009
DO - 10.1016/j.jvcir.2015.07.009
M3 - Article
AN - SCOPUS:84937808290
VL - 31
SP - 312
EP - 319
JO - Journal of Visual Communication and Image Representation
JF - Journal of Visual Communication and Image Representation
SN - 1047-3203
ER -