Abstract
With the development of social media and personal cloud service, everyone can upload their media data easily. According to statistics, the number of photos uploaded to Facebook can reach 300 million. With that, how to manage the personal photo album efficiently has becomes an issue. Face clustering plays as an important role in it. Face clustering is a technique to perform clustering on an unlabeled face dataset. Although face recognition has been well developed in the past decades that good representations can well describe faces. The difficulty of face clustering is that facial features may be very different for the same person or features maybe similar for different person due to various expression, lightning, poses. Face clustering only depends on visual feature often over-clustered (clusters with high precision but very low recall). Other than visual information of image itself, personal album contains other metadata like: whether faces are taken in same photo (co-occurrence); when the photo was taken ..., this additional information may lead to a better cluster result that visual information is not capable of. For instance, faces detected in the same photo must not be the same person, while photos in the same scene usually contain the same group of people. We call this kind of information “social information”. In this thesis, we will try to mine social information from a personal photo album and improve the purely visual based clustering result. Feeding back the results improved by social information to the feature extraction step, the initial clustering can provide results considering both visual and social information. Our experiments show that the clustering performance is improved by the iterative procedure.