A Robust Face Recognition Method Using Multiple Features Fusion and Linear Regression

This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. T...

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Bibliographic Details
Published inWuhan University journal of natural sciences Vol. 19; no. 4; pp. 323 - 327
Main Authors Gao, Zhirong, Ding, Lixin, Xiong, Chengyi, Huang, Bo
Format Journal Article
LanguageEnglish
Published Wuhan Wuhan University 01.08.2014
Subjects
Algorithms
Biomedical and Life Sciences
Computer Science
Face recognition
Feature extraction
Feature recognition
Illumination
Life Sciences
Materials Science
Regression
Transforms
Wavelet transforms
local feature
weighted fusion
TP 391.4
holistic feature
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Summary:This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.
Bibliography:This paper presents a robust face recognition algorithm by using transform domain-based multiple feature fusion and lin- ear regression. Transform domain-based feature fusion can provide comprehensive face information for recognition, and decrease the effect of variations in illumination and pose. The holistic feature and local feature are extracted by discrete cosine transform and Gabor wavelet transform, respectively. Then the extracted holistic features and the local features are fused by weighted sum. The fused feature values are finally sent to linear regression classifier for recognition. The algorithm is evaluated on AR, ORL and Yale B face databases. Experiment results show that our proposed algo- rithm could be more robust than those single feature-based algo- rithms under pose and expression variations.
42-1405/N
holistic feature; local feature; weighted fusion
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1007-1202
1993-4998
DOI:10.1007/s11859-014-1020-6