Proceedings ArticleDOI
Aiding face recognition with social context association rule based re-ranking
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TLDR
The results show that association rules extracted from social context can be used to augment face recognition and improve the identification performance.Abstract:
Humans are very efficient at recognizing familiar face images even in challenging conditions. One reason for such capabilities is the ability to understand social context between individuals. Sometimes the identity of the person in a photo can be inferred based on the identity of other persons in the same photo, when some social context between them is known. This research presents an algorithm to utilize cooccurrence of individuals as the social context to improve face recognition. Association rule mining is utilized to infer multi-level social context among subjects from a large repository of social transactions. The results are demonstrated on the G-album and on the SN-collection pertaining to 4675 identities prepared by the authors from a social networking website. The results show that association rules extracted from social context can be used to augment face recognition and improve the identification performance.read more
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References
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Clothing cosegmentation for recognizing people
Andrew C. Gallagher,Tsuhan Chen +1 more
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Autotagging Facebook: Social network context improves photo annotation
TL;DR: It is demonstrated that the simple method of enhancing face recognition with social network context substantially increases recognition performance beyond that of a baseline face recognition system.
Proceedings ArticleDOI
EasyAlbum: an interactive photo annotation system based on face clustering and re-ranking
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Book ChapterDOI
Seeing people in social context: recognizing people and social relationships
TL;DR: A model for representing the interaction between social relationship, facial appearance, and identity is introduced and it is shown that the family relationship a pair of people share influences the relative pairwise features between them.
Proceedings ArticleDOI
Towards context-aware face recognition
TL;DR: Context-aware media analysis is used to solve the face recognition problem for cameraphone photos by applying Sparse-Factor Analysis to both the contextual metadata gathered in the MMM2 system and the results of PCA of the photo content to achieve 60% face recognition accuracy.