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Face recognition using wavelet transform and Kernel Principal Component Analysis

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1 Author(s)
Xiang-fei Nie ; Coll. of Phys. & Electron. Eng., Chongqing Three Gorges Univ., Chongqing, China

A novel face recognition method using wavelet transform and Kernel Principal Component Analysis (KPCA) was presented. The method calculated logarithm transform and 2-dimensional wavelet transform for face pre-processing, used KPCA algorithm for face feature extraction, and adopted nearest neighborhood classifier based on Cosine distance for feature classification. The experimental results on Yale B frontal face database show that the face recognition rate of the proposed method can attain 100%. That is, the proposed approach can alleviate variable illumination for face recognition and identify all test samples on Yale B frontal face database accurately..

Published in:

Future Information Technology and Management Engineering (FITME), 2010 International Conference on  (Volume:3 )

Date of Conference:

9-10 Oct. 2010

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