Uncooled infrared imaging face recognition using kernel-based feature vector selection
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Authors
Alexandropoulos, Ioannis M.
Subjects
Advisors
Furgues, Monique P.
Cristi, Roberto
Borges, Carlos
Date of Issue
2006-09
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
A considerable amount of research has been recently conducted on face recognition tasks, due to increasing demands for security and authentication applications. Recent technological developments in uncooled IR imagery technology have boosted IR face recognition research applications. Our study is part of an on-going research initiated at the Naval Postgraduate School that considers an uncooled low-resolution and low-cost IR camera used for face recognition applications. This work investigates a recent approach which approximates nonlinear kernel-based methods at a significantly reduced computational cost. Our research was applied to an IR database. Results show that this scheme may perform sufficiently close to its â kernelizedâ version considered in a previous study, at a fraction of the computational cost, provided that the associated parameters are well tuned. The thesis considers a relative comparison between the two algorithms, based on identification and verification experiments and considers a statistical test to investigate whether classification performance differences may be considered statistically significant. Results show that, from a cost perspective, a low-resolution uncooled IR camera in conjunction with a low computational-cost classification scheme can be embedded in a robust face recognition system to efficiently address the issue of authentication in security-related tasks.
Type
Thesis
Description
Series/Report No
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
xvi, 137 p. : ill. (some col.) ;
Citation
Distribution Statement
Approved for public release; distribution is unlimited.