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.
Hierarchical Face Clustering using SIFT Image Features
01.04.2007
5245625 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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