Learning a subspace for face image clustering via trace ratio criterion

Face clustering is gaining ever-increasing attention due to its importance in optical image processing. Because traditional clustering methods do not specify the particular characters of the face image, they are not suitable for face image clustering. We propose a novel approach that employs the trace ratio criterion and specifies that the face images should be spatially smooth. The graph regularization technique is also applied to constrain that nearby images have similar cluster indicators. We alternately learn the optimal subspace and the clusters. Experimental results demonstrate that the proposed approach performs better than other learning methods for face image clustering.