Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/54835
Citations | ||
Scopus | Web of ScienceĀ® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | A new distance criterion for face recognition using image sets |
Author: | Chin, T. 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) |
Editor: | Narayanan, P. Nayar, S. Shum, H. |
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. |
DOI: | 10.1007/11612032_56 |
Published version: | http://dx.doi.org/10.1007/11612032_56 |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.