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An Efficient Incremental Face Annotation for Large Scale Web Services

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Executive Summary

This paper proposes an incremental face annotation framework for sharing and publishing photographs which contain faces under a large scale web platform such as a social network service with millions of users. Unlike the conventional face recognition environment addressed by most existing works, the image databases being accessed by the large pool of users can be huge and frequently updated. A reasonable way to efficiently annotate such huge databases is to accommodate an adaptation of model parameters without the need to retrain the model all over again when new data arrives.

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