Improved Wavelet-Based Illumination Normalization Algorithm for Face Recognition

The appearance of a face image is severely affected by illumination conditions that hinder the automatic face recognition process. To recognize faces under varying illuminations, we propose an improved wavelet-based normalization method so as to normalize illuminations. We decompose an image into its low frequency and high frequency components. Then different band coefficients are manipulated separately. We apply histogram equalization to the approximation (low frequency) coefficients and at the same time de-noise the detail (high frequency) coefficients adaptively and then accentuate them by multiplying by a scalar so as to enhance edges. A normalized image is obtained from the modified coefficients by inverse wavelet transform. Finally, 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.