This paper presents an online learning algorithm to construct from video sequences an image-based representation that is useful for recognition and tracking. For a class of objects (e.g., human faces), a generic representation of the appearances of the class is learned off-line. From video of an instance of this class (e.g., a particular person), an appearance model is incrementally learned on-line using the prior generic model and successive frames from the video. More specifically, both the generic and individual appearances are represented as an appearance manifold that is approximated by a collection of sub-manifolds (named pose manifolds) and the connectivity between them. In turn, each sub-manifold is approximated by a low-dimensional linear sub-space while the connectivity is modeled by transition probabilities between pairs of sub-manifolds. We demonstrate that our online learning algorithm constructs an effective representation for face tracking, and its use in video-based face recognition compares favorably to the representation constructed with a batch technique.


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    Titel :

    Online learning of probabilistic appearance manifolds for video-based recognition and tracking


    Beteiligte:
    Kuang-Chih Lee, (Autor:in) / Kriegman, D. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    409548 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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