Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Automated social hierarchy detection through email network analysis
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Measuring social networks with digital photograph collections
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
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
IEEE Transactions on Image Processing
Dynamic estimation of family relations from photos
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Discovering informative social subgraphs and predicting pairwise relationships from group photos
Proceedings of the 20th ACM international conference on Multimedia
Discovering relationship types between users using profiles and shared photos in a social network
Multimedia Tools and Applications
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In this paper, we propose an approach to automatically estimate relationship among people in a family image collection based on results from face analyses technologies including automated face recognition and clustering, demographic assessment, and face similarity measurement, as well as contextual information such as people co-appearance, people's relative positions in photos and image timestamps. As the result, a relation tree can be estimated which provides important semantic information regarding people involved in a photo collection and has numerous applications in photo sharing and browsing, social networking, etc. The methods for deriving and integrating information from photos and the process for estimating a relation tree are described. Experimental results on two typical consumer photo collections and examples of using these results in consumer image retrieval are presented.