Gabor Wavelet Based Modular PCA Approach for Expression and Illumination Invariant Face Recognition

A Gabor wavelet based modular PCA approach for face recognition is proposed in this paper. The proposed technique improves the efficiency of face recognition, under varying illumination and expression conditions for face images when compared to traditional PCA techniques. In this algorithm the face images are divided into smaller sub-images called modules and a series of Gabor wavelets at different scales and orientations are applied on these localized modules for feature extraction. A modified PCA approach is then applied for dimensionality reduction. Due to the extraction of localized features using Gabor wavelets, the proposed algorithm is expected to give improved recognition rate when compared to other traditional techniques. The performance of the proposed technique is evaluated under conditions of varying illumination, expression and variation in pose up to a certain range using standard face databases.