A Face Tracker Trajectories Clustering Using Mutual Information

In this paper we propose an algorithm for face tracker´s trajectories clustering. Our approach is based on the mutual information of the images and more precisely its normalized version (NMI). We make use of 2 color channels from the HSV space (hue and saturation) in order to calculate a 4D joint histogram and therefore calculate the mutual information. In this paper we also develop an algorithm where we apply robust heuristics and make use of a tracker information in order to diminish dimensionality and augment accuracy of our results. It is a supervised clustering algorithm which is therefore used (fuzzy c-means) in order to gather same trajectories and same faces together.