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Proceedings Paper

Face detection in thermal imagery using an Open Source Computer Vision library
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Paper Abstract

This paper studies the use of a combination of Haar-like features and a cascade of boosted tree classifiers embedded in a widely used OpenCV for face detection in thermal images. With 2013 positive and 2020 negative 320×240-pixel thermal images for 20 training stages on three window sizes of 20×20, 24×24, and 30×30 pixels, our experiment shows that these three windows offer similar hit and false alarm rates at the end of the training section. Larger windows also spend much more time to train. During our testing, the 30×30-pixel window provides measured best hit and false rejection/acceptation rates of 93.4% and 6.6%, respectively, with a measured slowest detection speed of 19.6 ms. A 5-ms improvement in the measured detection speed with a slightly lower hit rate of 92.1% is accomplished by using the 24×24-pixel window. These results verify that the combination of Haar-like features and a cascade of boosted tree classifiers is a promising technique for face detection application in thermal images.

Paper Details

Date Published: 22 April 2009
PDF: 6 pages
Proc. SPIE 7299, Thermosense XXXI, 729906 (22 April 2009); doi: 10.1117/12.819996
Show Author Affiliations
Sarun Sumriddetchkajorn, National Electronics and Computer Technology Ctr. (Thailand)
Armote Somboonkaew, National Electronics and Computer Technology Ctr. (Thailand)


Published in SPIE Proceedings Vol. 7299:
Thermosense XXXI
Douglas D. Burleigh; Ralph B. Dinwiddie, Editor(s)

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