Facial image clustering in 3D video using constrained Ncut

Abstract

In this paper a novel variant of the Normalized Nut (N-Cut) clustering algorithm that incorporates imposed constraints is implemented and evaluated on facial image clustering for 3D video analysis. The clustering problem is seen as a graph cut problem through a similarity matrix representing the relation among the vertices, i.e. facial images in this work. Mutual Information is used as similarity metric, applied on the HSV color space of the original images. This work considers the incorporation of constraints either regarding similarity or dissimilarity derived from a priori available information in the clustering procedure and evaluates the performance increase by their use. Experiments are conducted on 3D videos where a priori information about the facial images exists.