In this thesis, we present a system for face tagging recommendation using group prior on social network. We propose a simple but efficient approach of finding a group of people to recommend querying photo. In addition, most of the previous studies using social context for face tagging just only consider the relationship between the known face and the query faces. Thus, if a query face that has no close relationship with the known face, their methods cannot work well under this condition.
Instead, our approach can correctly tag the query faces not close with the known face via the relationships between query faces in the photo. So, the community detection is used to find a group of users which is close with known face although some users in the group are not familiar with known face. To improve the performance and the robustness of the system, some enhancements can be done in the future:
(i) Usage of text-based social context: in social websites, text-based social context has a lot of information that can be used for learning the relationship among network users. For instance, the profile page has information about users, such as:
gender, name of high school, occupation and interest. For using those information, we can assume that the people have high relationship if they are in the same school.
(ii) A user-friendly interface: a good user interface design can make face tagging easier. For example, when we want to tag a face in a photo, a recommendation list popping out can help us quickly find the target user instead of searching the user among all the users.
(iii) Improvement of face recognition: although the using of relationship can improves the accuracy of face recommendation, face recognition still is important for face tagging. An accurate face recognition approach can makes the face tagging recommendation system better.
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