Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms

Xiaoyuan Jing; Sheng Li; Dapeng Zhang; Jingyu Yang

doi:10.1109/ICIP.2011.6116307

Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms

Xiaoyuan Jing, Sheng Li, Dapeng Zhang, Jingyu Yang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

14 Citations (Scopus)

Abstract

Feature extraction is one of the most important problems in image recognition tasks. In many applications such as face recognition, it is desirable to eliminate the redundancy among the extracted discriminant features. In this paper, we propose two novel feature extraction approaches named local uncorrelated discriminant transform (LUDT) and weighted global uncorrelated discriminant transform (WGUDT) for face recognition, respectively. LUDT and WGUDT separately construct the local uncorrelated constraints and the weighted global uncorrelated constraints. Then they iteratively calculate the optimal discriminant vectors that maximize the Fisher criterion under the corresponding statistical uncorrelated constraints, respectively. The proposed LUDT and WGUDT approaches are evaluated on the public AR and FERET face databases. Experimental results demonstrate that the proposed approaches outperform several representative feature extraction methods.

Original language	English
Title of host publication	ICIP 2011
Subtitle of host publication	2011 18th IEEE International Conference on Image Processing
Pages	3049-3052
Number of pages	4
DOIs	https://doi.org/10.1109/ICIP.2011.6116307
Publication status	Published - 1 Dec 2011
Event	2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium Duration: 11 Sept 2011 → 14 Sept 2011

Conference

Conference	2011 18th IEEE International Conference on Image Processing, ICIP 2011
Country/Territory	Belgium
City	Brussels
Period	11/09/11 → 14/09/11

Keywords

face recognition
Feature extraction
local uncorrelated discriminant transform
uncorrelated constraints
weighted global uncorrelated discriminant transform

ASJC Scopus subject areas

Software
Computer Vision and Pattern Recognition
Signal Processing

More information

10.1109/ICIP.2011.6116307

Cite this

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title = "Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms",

abstract = "Feature extraction is one of the most important problems in image recognition tasks. In many applications such as face recognition, it is desirable to eliminate the redundancy among the extracted discriminant features. In this paper, we propose two novel feature extraction approaches named local uncorrelated discriminant transform (LUDT) and weighted global uncorrelated discriminant transform (WGUDT) for face recognition, respectively. LUDT and WGUDT separately construct the local uncorrelated constraints and the weighted global uncorrelated constraints. Then they iteratively calculate the optimal discriminant vectors that maximize the Fisher criterion under the corresponding statistical uncorrelated constraints, respectively. The proposed LUDT and WGUDT approaches are evaluated on the public AR and FERET face databases. Experimental results demonstrate that the proposed approaches outperform several representative feature extraction methods.",

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author = "Xiaoyuan Jing and Sheng Li and Dapeng Zhang and Jingyu Yang",

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doi = "10.1109/ICIP.2011.6116307",

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Jing, X, Li, S, Zhang, D & Yang, J 2011, Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms. in ICIP 2011: 2011 18th IEEE International Conference on Image Processing., 6116307, pp. 3049-3052, 2011 18th IEEE International Conference on Image Processing, ICIP 2011, Brussels, Belgium, 11/09/11. https://doi.org/10.1109/ICIP.2011.6116307

Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms. / Jing, Xiaoyuan; Li, Sheng; Zhang, Dapeng et al.
ICIP 2011: 2011 18th IEEE International Conference on Image Processing. 2011. p. 3049-3052 6116307.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - Feature extraction is one of the most important problems in image recognition tasks. In many applications such as face recognition, it is desirable to eliminate the redundancy among the extracted discriminant features. In this paper, we propose two novel feature extraction approaches named local uncorrelated discriminant transform (LUDT) and weighted global uncorrelated discriminant transform (WGUDT) for face recognition, respectively. LUDT and WGUDT separately construct the local uncorrelated constraints and the weighted global uncorrelated constraints. Then they iteratively calculate the optimal discriminant vectors that maximize the Fisher criterion under the corresponding statistical uncorrelated constraints, respectively. The proposed LUDT and WGUDT approaches are evaluated on the public AR and FERET face databases. Experimental results demonstrate that the proposed approaches outperform several representative feature extraction methods.

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KW - Feature extraction

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Face recognition based on local uncorrelated and weighted global uncorrelated discriminant transforms

Abstract

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Keywords

ASJC Scopus subject areas

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