Hierarchical Face Clustering

Clustering could be considered as a form of unsupervised classification imposed over a finite set of objects. Its goal is to group sets of objects into classes, such that similar objects are placed in the same cluster, while dissimilar objects are placed in different clusters.

Human faces are some of the most important and frequently encountered entities in videos and can be considered as high-level semantic features. Face clustering in videos can be used in many applications such as video indexing and content analysis, as a pre processing step for face recognition, or even as a basic step for extracting the principal cast of a feature length movie and much more.


Our Method

An algorithm to cluster face images found in feature length movies and generally 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 finally yields the clustering result.

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

Clusters 1, 2 and 4 contained only facial images from the same person. The third cluster contained the false face detections (non-facial images) as we expected, but it also included certain instances of the actor in cluster 1, due to a significant change in the person's pose.





Relevant Publications

P. Antonopoulos, N. Nikolaidis and I. Pitas, “Hierarchical Face Clustering Using SIFT Image Features”, submitted in Proc. of IEEE Symposium on Computational Intelligence in Image and Signal Processing (CIISP 2007), Honolulu, HI , USA.


Research Projects

Pythagoras II - Funded by the Hellenic Ministry of Education in the framework of the program


© 2006