Symmetrical Singular Value Decomposition Representation Under Illumination Face Image Using Gabor Filter For Face Recognition

Abstract

The purpose of this research is to present Symmetrical Singular Value Decomposition Representation (SSVDR) method with Gabor Filter under illumination effect for face recognition. SSVDR method was proposed to normalize illuminated face images caused by the difference of light. SSVDR method represented characteristics of face images on low-intensity combined with the symmetrical reversed part of the face based on Singular Value Decomposition (SVD). Gabor Filter was used to extract the face images which were already processed using SSVDR. In order to do the face recognition, PCA and LDA methods were used with Nearest Neighbor as the classifier. The result showed that face recognition with SSVDR based on Gabor Filter was quite good on face images with low-intensity, but the recognition were not good enough on face images with extreme illumination. Overall, the accuracy of face recognition scored 91.86% with PCA and 91.57% with LDA.