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Proceedings Paper

Mixed features for face detection in thermal image
Author(s): Chao Ma; Ngo Thanh Trung; Hideaki Uchiyama; Hajime Nagahara; Atsushi Shimada; Rin-ichiro Taniguchi
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Paper Abstract

An infrared (IR) camera captures the temperature distribution of an object as an IR image. Because facial temperature is almost constant, an IR camera has the potential to be used in detecting facial regions in IR images. However, in detecting faces, a simple temperature thresholding does not always work reliably. The standard face detection algorithm used is AdaBoost with local features, such as Haar-like, MB-LBP, and HoG features in the visible images. However, there are few studies using these local features in IR image analysis. In this paper, we propose an AdaBoost-based training method to mix these local features for face detection in thermal images. In an experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females, with 10 variations in camera distance, 21 poses, and participants with and without glasses. Using leave-one-out cross-validation, we show that the proposed mixed features have an advantage over all the regular local features.

Paper Details

Date Published: 14 May 2017
PDF: 8 pages
Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380E (14 May 2017); doi: 10.1117/12.2266836
Show Author Affiliations
Chao Ma, Kyushu Univ. (Japan)
Ngo Thanh Trung, Kyushu Univ. (Japan)
Hideaki Uchiyama, Kyushu Univ. (Japan)
Hajime Nagahara, Kyushu Univ. (Japan)
Atsushi Shimada, Kyushu Univ. (Japan)
Rin-ichiro Taniguchi, Kyushu Univ. (Japan)


Published in SPIE Proceedings Vol. 10338:
Thirteenth International Conference on Quality Control by Artificial Vision 2017
Hajime Nagahara; Kazunori Umeda; Atsushi Yamashita, Editor(s)

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