Social relationship discovery and face annotation in personal photo collection

As wider use of digital camera in these decades, photograph data from individuals increases dramatically. Many photos with different people are available on the Internet. It stimulates a strong demand on automatic face annotation. Moreover, it becomes more possible to discover potential social information from increasingly large photo collections. Every photo in a photo collection is not isolated. Instead, they are highly related as a whole to represent an event, such as a wedding. In a particular event, people would appear as a group following some rules, like families show up in a wedding and colleagues from the same research group in a conference. We also found that clues of closeness between people imply in photos as well. This paper explores social community from personal photo collection with modularity and proposes a method combining ensemble RBFNN with pairwise social relationship as context for recognizing people. Experiments on a conference photo album shows that a certain embedded social network with community structure is revealed. Our simple approach of face recognition with social context enhances the annotation performance when compared with the baseline method.