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Abstract: We address the problem of robust face identification in the presence of pose, lighting, and expression variation. Previous approaches to the problem have assumed similar models of variation for each individual, estimated from pooled training data. We describe a method of updating a first order global estimate of identity by learning the classspecific correlation between the estimate and the residual variation during a sequence. This is integrated with an optimal tracking scheme, in which... (Update)
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BibTeX entry: (Update)
G. J. Edwards, C. J. Taylor, and T. Cootes. Learning to identify and track faces in image sequences. In 8 th British Machine Vison Conference, pages 130--139, Colchester, UK, 1997. http://citeseer.ist.psu.edu/article/edwards97learning.html More
@inproceedings{ edwards98learning,
author = "Gareth J. Edwards and Christopher J. Taylor and Timothy F. Cootes",
title = "Learning to Identify and Track Faces in Image Sequences",
booktitle = "{ICCV}",
pages = "317-322",
year = "1998",
url = "citeseer.ist.psu.edu/article/edwards97learning.html" }
Citations (may not include all citations):412 Journal of Cognitive Neuroscience (context) - Turk, Pentland et al. - 1991
362 Active Shape Models - Their Training and Application (context) - Cootes, Taylor et al. - 1995
64 A Unified Approach to Coding and Interpreting Face Images - Lanitis, Taylor et al. - 1995
50 Discrimination and Classification (context) - Hand - 1981
23 Facial Analysis and Synthesis Using Image-Based Models - Ezzat, Poggio - 1997
19 Learning Deformable Models for Tracking Human Motion - Baumberg - 1995
12 Statistical Models of Face Images: Improving Specificity - Edwards, Lanitis et al. - 1996
7 Appliction of the Karhumen-Loeve Procedure for the Character.. (context) - Kirby, Sirovich - 1990
5 Principal Warps: Thin-Plate Splines and the Decompostion of .. (context) - Bookstein - 1989
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