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
2007-04-01
5245625 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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