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Recent research on face recognition shows that the illumination change is one of the key issues remaining to be addressed. To recognize faces under varying illuminations with single training image per person conditions, we propose an improved wavelet-based normalization method. We use wavelet transform to decompose an image into its low frequency and high frequency components. Then, we apply histogram equalization to the low frequency coefficients and de-noise the high frequency coefficients adaptively. Lastly, the high frequency coefficients are accentuated by multiplying by a scalar so as to enhance edges. A normalized image is obtained from the modified coefficients by inverse wavelet transform. Among others, the proposed method has the following advantages: (1) it does not need any prior information of 3D shape or light sources, and it aims at addressing illumination issue for face recognition from only one training image per person; (2) due to the multiscale nature of wavelet transform, it has better edge-preserving ability in low frequency illumination fields; and (3) it is computationally feasible and fast. We use PCA method to recognize normalized image with only one training image. The experimental results obtained by testing on the Yale face database B demonstrate the effectiveness of our method with significant improvement in the face recognition system.

AN IMPROVED ILLUMINATION NORMALIZATION APPROACH BASED ON WAVELET TRANFORM FOR FACE RECOGNITION FROM SINGLE TRAINING IMAGE PER PERSON