In this paper a new approach for face clustering is developed. Mutual information and joint entropy are exploited in order to create a metric for the clustering process. The way the joint entropy and the mutual information are calculated gives some interesting properties to the aforementioned metric, which guarantees some robustness against standard noisy transformation such as scaling, cropping and pose changes. A slight preprocessing of the input face images is done in order to undertake problems that arise from detector's known errors.