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Hierarchical Face Clustering using SIFT Image Features

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3 Author(s)
Antonopoulos, P. ; Dept. of Informatics, Aristotle Univ. of Thessaloniki ; Nikolaidis, N. ; Pitas, I.

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 hierarchical 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 Gamma statistic. The final result is found to be quite robust to significant scale, pose and illumination variations, encountered in facial images

Published in:

Computational Intelligence in Image and Signal Processing, 2007. CIISP 2007. IEEE Symposium on

Date of Conference:

1-5 April 2007