We present a general framework for face modeling under varying lighting conditions. First, we show that a face lighting subspace can be constructed based on three or more training face images illuminated by noncoplanar lights. The lighting of any face image can be represented as a point in this subspace. Second, we show that the extreme rays, i.e. the boundary of an illumination cone, cover the entire light sphere. Therefore, a relatively sparsely sampled face images can be used to build a face model instead of calculating each extremely illuminated face image. Third, we present a face normalization algorithm, illumination alignment, i.e. changing the lighting of one face image to that of another face image. Experiments are presented.


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    Title :

    Illumination modeling and normalization for face recognition


    Contributors:
    Haitao Wang, (author) / Li, S.Z. (author) / Yangsheng Wang, (author) / Weiwei Zhang, (author)


    Publication date :

    2003-01-01


    Size :

    754864 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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