Mixed features for face detection in thermal image

Chao Ma; Ngo Thanh Trung; Hideaki Uchiyama; Hajime Nagahara; Atsushi Shimada; Rin-ichiro Taniguchi

doi:10.1117/12.2266836

14 May 2017 Mixed features for face detection in thermal image

Chao Ma, Ngo Thanh Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, Rin-ichiro Taniguchi

Proceedings Volume 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017; 103380E (2017) https://doi.org/10.1117/12.2266836
Event: The International Conference on Quality Control by Artificial Vision 2017, 2017, Tokyo, Japan

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.

Citation Download Citation

Chao Ma, Ngo Thanh Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi "Mixed features for face detection in thermal image", Proc. SPIE 10338, Thirteenth International Conference on Quality Control by Artificial Vision 2017, 103380E (14 May 2017); https://doi.org/10.1117/12.2266836

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