NDLI: Names and faces in the news

Content Provider	IEEE Xplore Digital Library
Author	Berg, T.L. Berg, A.C. Edwards, J. Maire, M. White, R. Yee-Whye Teh Learned-Miller, E. Forsyth, D.A.
Copyright Year	2004
Description	Author affiliation: Div. of Comput. Sci., California Univ., Berkeley, CA, USA (Berg, T.L.; Berg, A.C.; Edwards, J.; Maire, M.; White, R.; Yee-Whye Teh; Learned-Miller, E.; Forsyth, D.A.)
Abstract	We show quite good face clustering is possible for a dataset of inaccurately and ambiguously labelled face images. Our dataset is 44,773 face images, obtained by applying a face finder to approximately half a million captioned news images. This dataset is more realistic than usual face recognition datasets, because it contains faces captured "in the wild" in a variety of configurations with respect to the camera, taking a variety of expressions, and under illumination of widely varying color. Each face image is associated with a set of names, automatically extracted from the associated caption. Many, but not all such sets contain the correct name. We cluster face images in appropriate discriminant coordinates. We use a clustering procedure to break ambiguities in labelling and identify incorrectly labelled faces. A merging procedure then identifies variants of names that refer to the same individual. The resulting representation can be used to label faces in news images or to organize news pictures by individuals present. An alternative view of our procedure is as a process that cleans up noisy supervised data. We demonstrate how to use entropy measures to evaluate such procedures.
Sponsorship	IEEE Comput. Soc
File Size	1420350
File Format	PDF
ISBN	0769521584
ISSN	10636919
DOI	10.1109/CVPR.2004.1315253
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Noise Entropy
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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