TY - GEN
T1 - Mixed features for face detection in thermal image
AU - Ma, Chao
AU - Trung, Ngo Thanh
AU - Uchiyama, Hideaki
AU - Nagahara, Hajime
AU - Shimada, Atsushi
AU - Taniguchi, Rin Ichiro
N1 - Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85020312468&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020312468&partnerID=8YFLogxK
U2 - 10.1117/12.2266836
DO - 10.1117/12.2266836
M3 - Conference contribution
AN - SCOPUS:85020312468
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Thirteenth International Conference on Quality Control by Artificial Vision 2017
A2 - Yamashita, Atsushi
A2 - Nagahara, Hajime
A2 - Umeda, Kazunori
PB - SPIE
T2 - 13th International Conference on Quality Control by Artificial Vision, QCAV 2017
Y2 - 14 May 2017 through 16 May 2017
ER -