Separability Oriented Preprocessing for Illumination-Insensitive Face Recognition

In the last decade, some illumination preprocessing approaches were proposed to eliminate the lighting variation in face images for lighting-invariant face recognition. However, we find surprisingly that existing preprocessing methods were seldom modeled to directly enhance the separability of different faces, which should have been the essential goal. To address the issue, we propose to explicitly exploit maximizing separability of different subjects’ faces as the preprocessing objective. With this in mind, a novel approach, named by us Separability Oriented Preprocessing (SOP), is proposed to enhance face images by maximizing the Fisher separability criterion in scale-space. Extensive experiments on both laboratory-controlled and real-world face databases using different recognition methods show the effectiveness of the proposed approach.