Content-based people clustering is a crucial step for people indexing within video documents. In this paper, we investigate the use of both face and clothing features. A method of extracting a keyface for each video sequence is proposed. An algorithm based on the average of the N-minimum pair distances between local invariant features is used in order to resolve the problem of face matching. An original method for clothing matching is proposed based on 3D histogram of the dominant color. A 3-levels hierarchical bottom-up clustering that combines local invariant features, skin color, 3D histogram and clothing texture is also described. Experiments and results show the efficiency of the proposed clustering system.