Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/54835
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Type: Conference paper
Title: A new distance criterion for face recognition using image sets
Author: Chin, T.J.
Suter, D.
Citation: Proceedings of the 7th Asian Conference on Computer Vision, Hyderabad, India, 2006: pp.549-558
Publisher: Springer-Verlag
Publisher Place: Berlin
Issue Date: 2006
Series/Report no.: Lecture Notes in Computer Science; Volume 3851
ISBN: 3540312196
9783540312192
ISSN: 0302-9743
1611-3349
Conference Name: Asian Conference on Computer Vision (7th : 2006 : Hyderabad, India)
Statement of
Responsibility: 
Tat-Jun Chin and David Suter
Abstract: A major face recognition paradigm involves recognizing a person from a set of images instead of from a single image. Often, the image sets are acquired from a video stream by a camera surveillance system, or a combination of images which can be non-contiguous and unordered. An effective algorithm that tackles this problem involves fitting low-dimensional linear subspaces across the image sets and using a linear subspace as an approximation for the particular face identity. Unavoidably, the individual frames in the image set will be corrupted by noise and there is a degree of uncertainty on how accurate the resultant subspace approximates the set. Furthermore, when we compare two linear subspaces, how much of the distance between them is due to inter-personal differences and how much is due to intra-personal variations contributed by noise? Here, we propose a new distance criterion, developed based on a matrix perturbation theorem, for comparing two image sets that takes into account the uncertainty of estimating a linear subspace from noise affected image sets.
RMID: 0020093455
DOI: 10.1007/11612032_56
Appears in Collections:Computer Science publications

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