Abstract :
We propose a method to associate names and faces for querying people in large news photo collections. On the assumption that a person’s face is likely to appear when his/her name is mentioned in the caption, first all the faces associated with the query name are selected. Among these faces, there could be many faces corresponding to the queried person in different conditions, poses and times, but there could also be other faces corresponding to other people in the caption or some non-face images due to the errors in the face detection method used. However, in most cases, the number of corresponding faces of the queried person will be large, and these faces will be more similar to each other than to others. In this study, we propose a graph based method to find the most similar subset among the set of possible faces associated with the query name, where the most similar subset is likely to correspond to the faces of the queried person. When the similarity of faces are represented in a graph structure, the set of most similar faces will be the densest component in the graph. We represent the similarity of faces using SIFT descriptors. The matching interest points on two faces are decided after the application of two constraints, namely the geometrical constraint and the unique match constraint. The average distance of the matching points are used to construct the similarity graph. The most similar set of faces is then found based on a greedy densest component algorithm. The experiments are performed on thousands of news photographs taken in real life conditions and, therefore, having a large variety of poses, illuminations and expressions.
