Face recognition based on singular value decomposition and discriminant KL projection

De Long Zhou; Wen Gao; De Bin Zhao

Face recognition based on singular value decomposition and discriminant KL projection

De Long Zhou^*
, Wen Gao
, De Bin Zhao

^*Corresponding author for this work

Faculty of Computing

University of Chinese Academy of Sciences
CAS - Institute of Computing Technology
Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

16 Scopus citations

Abstract

The face recognition is an active subject in the fields of computer vision and pattern recognition, which has a wide range of potential applications. A method for color face recognition is presented, this algorithm extracts the final features by utilizing the techniques of the simulative K-L transform, the singular value decomposition, the principal component analysis and the Fisher linear discriminant analysis. Classifier in this algorithm can be simplified to make it more compact and effective, and higher correct recognition rate can be gained using less number of feature vectors. The effectiveness of the approach is experimentally demonstrated.

Original language	English
Pages (from-to)	783-789
Number of pages	7
Journal	Ruan Jian Xue Bao/Journal of Software
Volume	14
Issue number	4
State	Published - Apr 2003

Keywords

Face recognition
Feature extraction
Fisher linear discriminant analysis
K-L transform
Principal component analysis
Singular value feature vector

Cite this

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title = "Face recognition based on singular value decomposition and discriminant KL projection",

abstract = "The face recognition is an active subject in the fields of computer vision and pattern recognition, which has a wide range of potential applications. A method for color face recognition is presented, this algorithm extracts the final features by utilizing the techniques of the simulative K-L transform, the singular value decomposition, the principal component analysis and the Fisher linear discriminant analysis. Classifier in this algorithm can be simplified to make it more compact and effective, and higher correct recognition rate can be gained using less number of feature vectors. The effectiveness of the approach is experimentally demonstrated.",

keywords = "Face recognition, Feature extraction, Fisher linear discriminant analysis, K-L transform, Principal component analysis, Singular value feature vector",

author = "Zhou, \{De Long\} and Wen Gao and Zhao, \{De Bin\}",

year = "2003",

month = apr,

language = "英语",

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journal = "Ruan Jian Xue Bao/Journal of Software",

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publisher = "Chinese Academy of Sciences",

number = "4",

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TY - JOUR

T1 - Face recognition based on singular value decomposition and discriminant KL projection

AU - Zhou, De Long

AU - Gao, Wen

AU - Zhao, De Bin

PY - 2003/4

Y1 - 2003/4

N2 - The face recognition is an active subject in the fields of computer vision and pattern recognition, which has a wide range of potential applications. A method for color face recognition is presented, this algorithm extracts the final features by utilizing the techniques of the simulative K-L transform, the singular value decomposition, the principal component analysis and the Fisher linear discriminant analysis. Classifier in this algorithm can be simplified to make it more compact and effective, and higher correct recognition rate can be gained using less number of feature vectors. The effectiveness of the approach is experimentally demonstrated.

AB - The face recognition is an active subject in the fields of computer vision and pattern recognition, which has a wide range of potential applications. A method for color face recognition is presented, this algorithm extracts the final features by utilizing the techniques of the simulative K-L transform, the singular value decomposition, the principal component analysis and the Fisher linear discriminant analysis. Classifier in this algorithm can be simplified to make it more compact and effective, and higher correct recognition rate can be gained using less number of feature vectors. The effectiveness of the approach is experimentally demonstrated.

KW - Face recognition

KW - Feature extraction

KW - Fisher linear discriminant analysis

KW - K-L transform

KW - Principal component analysis

KW - Singular value feature vector

UR - https://www.scopus.com/pages/publications/0038727849

M3 - 文章

AN - SCOPUS:0038727849

SN - 1000-9825

VL - 14

SP - 783

EP - 789

JO - Ruan Jian Xue Bao/Journal of Software

JF - Ruan Jian Xue Bao/Journal of Software

IS - 4

ER -

Face recognition based on singular value decomposition and discriminant KL projection

Abstract

Keywords

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