A gender classification system robust to occlusion using Gabor features based (2D)2PCA
Develop a gender classification system for both occlusion free and occluded faces. In occlusion free case, it enhances the classification accuracy and speed. System renders accuracies above 90% under lower occlusion conditions. System also survive in higher occlusion conditions by giving minimum of 86% accuracy. We analyze the impact of various face parts in the context of gender classification.
occlusion real gabor space approximation face sub-image (2d)2 ( 2 d ) 2 pca (2d)2 ( 2 d ) 2 (2d)2 ( 2 d ) 2 ( 2 d ) 2 support vector machine region of interest local features entropy