In this paper an algorithm to cluster face images found in video sequences is proposed. A novel method for creating a dissimilarity matrix using SIFT image features is introduced. This dissimilarity matrix is used as an input in a hierar-chical average linkage clustering algorithm, which yields the clustering result. Three well known clustering validity measures are provided to asses the quality of the resulting clustering, namely the F measure, the overall entropy (OE) and the ¿ statistic. The final result is found to be quite robust to significant scale, pose and illumination variations, encountered in facial images.


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    Titel :

    Hierarchical Face Clustering using SIFT Image Features


    Beteiligte:


    Erscheinungsdatum :

    2007-04-01


    Format / Umfang :

    5245625 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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