Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16599
Title: An efficient illumination normalization method for face recognition
Authors: Xie, X
Lam, KM 
Keywords: Face recognition
Gabor wavelets
Illumination compensation
Independent component analysis (ICA)
Local normalization method
Principal component analysis (PCA)
Issue Date: 2006
Publisher: North-Holland
Source: Pattern recognition letters, 2006, v. 27, no. 6, p. 609-617 How to cite?
Journal: Pattern recognition letters 
Abstract: In this paper, an efficient representation method insensitive to varying illumination is proposed for human face recognition. Theoretical analysis based on the human face model and the illumination model shows that the effects of varying lighting on a human face image can be modeled by a sequence of multiplicative and additive noises. Instead of computing these noises, which is very difficult for real applications, we aim to reduce or even remove their effect. In our method, a local normalization technique is applied to an image, which can effectively and efficiently eliminate the effect of uneven illuminations while keeping the local statistical properties of the processed image the same as in the corresponding image under normal lighting condition. After processing, the image under varying illumination will have similar pixel values to the corresponding image that is under normal lighting condition. Then, the processed images are used for face recognition. The proposed algorithm has been evaluated based on the Yale database, the AR database, the PIE database, the YaleB database and the combined database by using different face recognition methods such as PCA, ICA and Gabor wavelets. Consistent and promising results were obtained, which show that our method can effectively eliminate the effect of uneven illumination and greatly improve the recognition results.
URI: http://hdl.handle.net/10397/16599
ISSN: 0167-8655
EISSN: 1872-7344
DOI: 10.1016/j.patrec.2005.09.026
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