Face recognition using Bidirectional Principal Component Analysis and wavelet transform

A novel face recognition method using wavelet transform and Bidirectional Principal Component Analysis (BDPCA) was presented. In the proposed method, the logarithm transform and wavelet transform were calculated for face preprocessing. BDPCA algorithm was used for face feature extraction. Finally, the nearest neighborhood classifier using Cosine distance was adopted for feature classification. The experimental results on Yale B frontal face database show that the face recognition rate of the proposed approach can attain 100% when wavelet type and wavelet decomposing levels were selected properly, and the proposed algorithm can alleviate face uneven illumination efficiently.