Adaptive Shadow Compensation for Face Recognition Under Illumination Variations
Cheng-Ta Hsieh, Kae-Horng Huang, Chang-Hsing Lee, Chin‐Chuan Han, Kuo‐Chin Fan · 2016
Robust face recognition under variant illumination conditions is a challenging research problem. In this paper, a new shadow compensation method, called adaptive shadow compensation (ASC), will be proposed to eliminate shadows in the face image due to non-frontal lighting directions. ASC performs shadow compensation in each local region, based on Fourier analysis on each local image block, to remove as much shadows as possible. Null-space linear discriminant analysis is then employed to extract discriminant features from ASC compensated images. Experimental results on the Yale B face database show that the proposed ASC method can achieve high face recognition accuracy under illumination variations.