Face detection in an image sequence is a challenging problem for many applications. In this paper, a novel face detection method is proposed. In order to detect faces in a sequence, based on Bayesian decision theory, we construct a unified framework of most face-like region selection, face/non-face classification, and detection result correction. And we propose face probability gradient ascent method to estimate the optimal position, scale, and rotation parameters of each face. In the experimental results, it is shown that the proposed method is more accurate and efficient than other conventional detection methods.


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    Titel :

    Bayesian face detection in an image sequence using face probability gradient ascent


    Beteiligte:
    Jae Hee Park, (Autor:in) / Hae Chul Choi, (Autor:in) / Seong Dae Kim, (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    150911 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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