摘要
A fusion method of Gabor features and (2D)~2LDA for face feature extraction is proposed in this paper. Gabor filters are utilized to extract multi-direction and multi-scale features from facial image to employ its robust performance for illumination,expressional variability and other factors. The extracted features have the defect of high dimension and redundancy data.(2D)~2LDA is implemented to reduce the dimension of Gabor features and select effective feature data. Finally, the nearest neighbor classifier is used to classify characteristics and complete face recognition. The experiments are implemented by using ORL database and Yale database respectively. The experimental results show that the proposed method significantly reduces the dimension of Gabor features and decrease the influence of other factors. The proposed method acquires excellent recognition accuracy and has light architectures as well.
A fusion method of Gabor features and （2D）^2LDA for face feature extraction is proposed in this paper. Gabor filters are utilized to extract multi-direction and multi-scale features from facial image to employ its robust performance for illumination,expressional variability and other factors. The extracted features have the defect of high dimension and redundancy data.（2D）^2LDA is implemented to reduce the dimension of Gabor features and select effective feature data. Finally, the nearest neighbor classifier is used to classify characteristics and complete face recognition. The experiments are implemented by using ORL database and Yale database respectively. The experimental results show that the proposed method significantly reduces the dimension of Gabor features and decrease the influence of other factors. The proposed method acquires excellent recognition accuracy and has light architectures as well.
基金
Sponsored by the National Natural Science Foundation of China(Grant No.61172167)
the Natural Science Foundation of Heilongjiang Province of China(Grant No.F201311)
