Self Quotient Image For Face Recognition (TA-P2)

Author(s) : Haitao Wang (Institute of Automation, Chinese Academy of Sciences, China) Stan Z. Li (Microsoft Research Asia, China) Yangsheng Wang (Institute of Automation, Chinese Academy of Sciences, China) Jian J. Zhang (NCCA, Media School, Bournemouth University, UK)

Abstract : The reliability of facial recognition techniques is often affected by the variation of illumination, such as shadows and illumination direction changes. In this paper, we present a novel framework, called the self-quotient image, for the elimination of the lighting effect in the image. Although this method has a similar invariant form to the quotient image by Shashua etc [1], it does not need the alignment and bootstrap images. Our method combines the image processing technique of edge-preserved filtering with the Retinex applications of by Jobson, et al [2] and Gross and Brajovie [3]. We have analyzed this algorithm with a 3D imaging model and formulated the conditions where illumination-invariant and ¨Cvariant properties can be realized, respectively. A fast anisotropic filter is also presented. The experiment results show that our method is effective in removing the effect of illumination for robust face recognition.

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