We propose a novel representation for characterizing image differences using a deformable technique for obtaining pixel-wise correspondences. This representation, which is based on a deformable 3D mesh in XYI-space, is then experimentally compared with two related correspondence methods: optical flow and intensity differences. Furthermore, we make use of a probabilistic similarity measure for direct image matching based on a Bayesian analysis of image variations. We model two classes of variation in facial appearance: intra-personal and extra-personal. The probability density function for each class is estimated from training data and used to compute a similarity measure based on the a posteriori probabilities. The performance advantage of our deformable probabilistic matching technique is demonstrated using 1700 faces from the USA Army's "FERET" face database.


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    Titel :

    Bayesian face recognition with deformable image models


    Beteiligte:
    Moghaddam, B. (Autor:in) / Nastar, C. (Autor:in) / Pentland, A. (Autor:in)


    Erscheinungsdatum :

    01.01.2001


    Format / Umfang :

    1145881 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Optical Flow Constraints on Deformable Models with Applications to Face Tracking

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    Bayesian Face Recognition Using Support Vector Machine and Face Clustering

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