Improving identification performance by integrating evidence fromsequences
Edwards, G.J.
Taylor, C.J.
Cootes, T.F.
Wolfson Image Anal. Unit, Manchester Univ.;
This paper appears in: Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on.
Publication Date: 1999
Volume: 1,
On page(s): -491 Vol. 1
Meeting Date: 06/23/1999 - 06/25/1999
Location: Fort Collins, CO, USA
ISBN: 0-7695-0149-4
References Cited: 10
INSPEC Accession Number: 6338065
Digital Object Identifier: 10.1109/CVPR.1999.786982
Current Version Published: 2002-08-06
Abstract
We present a quantitative evaluation of an algorithm for
model-based face recognition. The algorithm actively learns how
individual faces vary through video sequences, providing on-line
suppression of confounding factors such as expression, lighting and
pose. By actively decoupling sources of image variation, the algorithm
provides a framework in which identity evidence can be integrated over a
sequence. We demonstrate that face recognition can be considerably
improved by the analysis of video sequences. The method presented is
widely applicable in many multi-class interpretation problems
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