In many automatic face recognition applications, a set of a person's face images is available rather than a single image. In this paper, we describe a novel method for face recognition using image sets. We propose a flexible, semi-parametric model for learning probability densities confined to highly non-linear but intrinsically low-dimensional manifolds. The model leads to a statistical formulation of the recognition problem in terms of minimizing the divergence between densities estimated on these manifolds. The proposed method is evaluated on a large data set, acquired in realistic imaging conditions with severe illumination variation. Our algorithm is shown to match the best and outperform other state-of-the-art algorithms in the literature, achieving 94% recognition rate on average.


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    Titel :

    Face recognition with image sets using manifold density divergence


    Beteiligte:
    Arandjelovic, O. (Autor:in) / Shakhnarovich, G. (Autor:in) / Fisher, J. (Autor:in) / Cipolla, R. (Autor:in) / Darrell, T. (Autor:in)


    Erscheinungsdatum :

    2005-01-01


    Format / Umfang :

    416834 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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