Abstract: Face clustering which aims to automatically divide face images of the same identity into the same cluster,can be applied in a wide range of applications such as face annotation,image management,etc.The traditional face clustering algorithms can achieve good precision,but low recall.To handle this issue,this paper proposed a novel clustering algorithm with triangular constraints and context constraints.The proposed algorithm based on conditional random field model takes triangular constraints as well as common context constraints into accountin images.During the clustering iteration and after preliminary clustering,maximum similarity and people co-occurrence constraints are considered to merge the initial clusters.Experimental results reveal that the proposed face clustering algorithm can group faces efficiently,and improve recall with the high precision,and accordingly enhance the overall clustering performance.

Key words: Conditional random field, Context constraints, Face clustering, Triangular constraints