Face Recognition Method Using Gabor Wavelet and Cross-covariance Dimensionality Reduction
LI Yaqian① ZHANG Shaowei① LI Haibin① ZHANG Wenming① ZHANG Qiang①②
①(Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China) ②(College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)
The traditional face recognition is sensitive to light condition as well as facial expression, and has a shortcoming of high intra-group dispersion, a novel method is proposed to overcome these defects by combining Gabor wavelet and a weighted computation based on the cross-covariance. Firstly, Gabor features are extracted from the face image. Then, a weighted cross-covariance matrix is used for dimension reduction and feature extraction. Finally, the nearest neighbor classifier is performed for classification. Experimental results on the ORL face database and the AR face database show that the recognition performance of the proposed method is superior over the 2DPCA and its improved algorithm. It also reduces the dimensionality of feature and improves the recognition performance effectively.
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