Parallel Gabor Pca With Fusion Of Svm Scores For Face Verification

Here we present a novel fusion technique for support vector machine (SVM) scores, obtained after a dimension reduction with a principal component analysis algorithm (PCA) for Gabor features applied to face verification. A total of 40 wavelets (5 frequencies, 8 orientations) have been convolved with public domain FRAV2D face database (109 subjects), with 4 frontal images with neutral expression per person for the SVM training and 4 different kinds of tests, each with 4 images per person, considering frontal views with neutral expression, gestures, occlusions and changes of illumination. Each set of wavelet-convolved images is considered in parallel or independently for the PCA and the SVM classification. A final fusion is performed taking into account all the SVM scores for the 40 wavelets. The proposed algorithm improves the Equal Error Rate for the occlusion experiment compared to a Downsampled Gabor PCA method and obtains similar EERs in the other experiments with fewer coefficients after the PCA dimension reduction stage.