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...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.展开更多
基金Supported by the National Science Foundation of China(60972081)Hubei Natural Science Foundation of China(2013CFC118,2009CDA139)+3 种基金Special Funds for Shenzhen Strategic New Industry Development(JCYJ 20120616135936123)Special Project on the Integration of Industry,Education and Research of Ministry of Education of Guangdong Province(2011B090400477)Special Project on the Integration of Industry,Education and Research of Zhuhai City(2011A050101005,2012D0501990016)Zhuhai Key Laboratory Program for Science and Technique(2012D050 1990026)
文摘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.
