Investigation of uncooled infrared imagery for face recognition
Pereira, Diogo C.
Fargues, Monique P.
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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.
The article of record as published may be found at https://doi.org/10.1117/1.2151787
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
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