Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Automated annotation of human faces in family albums
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Leveraging context to resolve identity in photo albums
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Towards context-aware face recognition
Proceedings of the 13th annual ACM international conference on Multimedia
EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Face based image navigation and search
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Close & closer: social cluster and closeness from photo collections
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Graph-based recognition in photo collections using social semantics
SBNMA '11 Proceedings of the 2011 ACM workshop on Social and behavioural networked media access
Facing scalability: Naming faces in an online social network
Pattern Recognition
Improving face recognition with genealogical and contextual data
Proceedings of the 27th Conference on Image and Vision Computing New Zealand
A unified framework for context assisted face clustering
Proceedings of the 3rd ACM conference on International conference on multimedia retrieval
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In this paper we describe an algorithm to improve the performance of face clustering using the social relationship of people. One common challenge in face clustering techniques is that very often the faces of the same person are clustered into different face clusters, due to the imperfection of the face features. The remedy to this problem, the user needs to scan all the clusters and manually merge the face clusters of the same person to the same cluster. We propose to use the social context information inherent among the people in a collection to build a social network and combine this knowledge with face similarity measure to generate a small number of ranked face clusters as the candidate for a cluster to be merged to. Thus, a user can gain the benefit of often avoiding browsing the face clusters back and forth to find the right cluster to merge. Experimental results show that the proposed approach can improve the recall of face clustering because more correct faces are merged into their significant cluster while still maintaining a high precision.