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Optical Engineering

Investigation of uncooled infrared imagery for face recognition
Author(s): Diogo C. Pereira; Monique P. Fargues; Gamani Karunasiri
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Paper Abstract

Recent advances in uncooled infrared technology have resulted in thermal imagers with resolution approaching that of cooled counterparts at a significantly lower cost. We investigate the application of linear classification schemes to a database consisting of 420 images collected from 14 adult subjects using an uncooled infrared camera under indoor controlled conditions. Results show that the linear discriminant approach (LDA) leads to the best classification performances (99.3%), while the best principal component analysis (PCA)-based scheme leads to an accuracy of 91.33%. Results also show that PCA-based classification scheme performance improves by removing the top three eigenvectors, associated with the three largest eigenvalues, from consideration in the generation of the PCA projection matrix for the small database considered in this study, as was noted in visible imaging face recognition studies.

Paper Details

Date Published: 1 January 2006
PDF: 6 pages
Opt. Eng. 45(1) 016401 doi: 10.1117/1.2151787
Published in: Optical Engineering Volume 45, Issue 1
Show Author Affiliations
Diogo C. Pereira, Ctr. Técnico Aeroespacial (Brazil)
Monique P. Fargues, Naval Postgraduate School (United States)
Gamani Karunasiri, Naval Postgraduate School (United States)

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