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Collaborative filtering with privacy via factor analysis
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Context and Memory in Multimedia Content Analysis
IEEE MultiMedia
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Proceedings of the 12th annual ACM international conference on Multimedia
MMM2: mobile media metadata for media sharing
CHI '05 Extended Abstracts on Human Factors in Computing Systems
The uses of personal networked digital imaging: an empirical study of cameraphone photos and sharing
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Leveraging context to resolve identity in photo albums
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Collective privacy management in social networks
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Improving face clustering using social context
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The VLDB Journal — The International Journal on Very Large Data Bases
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ACRONYM: Context Metrics for Linking People to User-Generated Media Content
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Computationally Significant Semantics in Pervasive Healthcare
Journal of Integrated Design & Process Science
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In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata together with computer vision analysis of image content to make accurate predictions about the human subjects depicted in cameraphone photos. We apply Sparse-Factor Analysis (SFA) to both the contextual metadata gathered in the MMM2 system and the results of PCA (Principal Components Analysis) of the photo content to achieve a 60% face recognition accuracy of people depicted in our cameraphone photos, which is 40% better than media analysis alone. In short, we use context-aware media analysis to solve the face recognition problem for cameraphone photos.