Mixed features for face detection in thermal image

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationThirteenth International Conference on Quality Control by Artificial Vision 2017
EditorsAtsushi Yamashita, Hajime Nagahara, Kazunori Umeda
PublisherSPIE
ISBN (Electronic)9781510611214
DOIs
Publication statusPublished - Jan 1 2017
Event13th International Conference on Quality Control by Artificial Vision, QCAV 2017 - Tokyo, Japan
Duration: May 14 2017May 16 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10338
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Other

Other13th International Conference on Quality Control by Artificial Vision, QCAV 2017
CountryJapan
CityTokyo
Period5/14/175/16/17

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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  • Cite this

    Ma, C., Trung, N. T., Uchiyama, H., Nagahara, H., Shimada, A., & Taniguchi, R. I. (2017). Mixed features for face detection in thermal image. In A. Yamashita, H. Nagahara, & K. Umeda (Eds.), Thirteenth International Conference on Quality Control by Artificial Vision 2017 [1033805] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10338). SPIE. https://doi.org/10.1117/12.2266836