Abstract
The performances of the current face recognition systems suffer heavily from the variations in lighting. To deal with this problem, this paper presents an illumination normalization approach by relighting face images to a canonical illumination based on the harmonic images model. Benefiting from the observations that human faces share similar shape, and the albedos of the face surfaces are quasi-constant, we first estimate the nine low-frequency components of the illumination from the input facial image. The facial image is then normalized to the canonical illumination by re-rendering it using the illumination ratio image technique. For the purpose of face recognition, two kinds of canonical illuminations, the uniform illumination and a frontal flash with the ambient lights, are considered, among which the former encodes merely the texture information, while the latter encodes both the texture and shading information. Our experiments on the CMU-PIE face database and the Yale B face database have shown that the proposed relighting normalization can significantly improve the performance of a face recognition system when the probes are collected under varying lighting conditions.
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