Clustering of specific object detections is a challenging problem for video summarization. In this article, we present a method to form tracks by grouping face detections of a video sequence. Our clustering method is based on a probabilistic maximum a posteriori data association framework, and we apply it to face detection in a visual surveillance context. Optimal solution is found with a procedure using network-flow algorithms described in previous pedestrian tracking-by-detection works. To address difficult cases of small detections in scenes with multiple moving people, given that face detections are located in a video sequence, we use dissimilarities involving appearance and spatio-temporal information. The main contribution is the use of an optical flow or local front-back tracking to handle complex situations appearing in real sequences. The resulting algorithm is then able to deal with situations where people are crossing one another and face detections are scattered due to head rotation. The clustering step of our framework is compared to generic clustering methods (hierarchical clustering and affinity propagation) on several real challenging sequences, as evaluations indicate that this is more adapted to video-based detection clustering. We propose to use a new evaluation criteria, derived from purity and inverse purity of a clustering estimation, to assess performances of such methods. Results also show that optical flow and a skin color prior added to face detections improve the clustering quality.