Next Article in Journal
All-Fiber Laser Curvature Sensor Using an In-Fiber Modal Interferometer Based on a Double Clad Fiber and a Multimode Fiber Structure
Previous Article in Journal
Analysis of Sources of Large Positioning Errors in Deterministic Fingerprinting
Previous Article in Special Issue
Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(12), 2741; doi:10.3390/s17122741

Adapting Local Features for Face Detection in Thermal Image

Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan
The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan
Institute for Datability Science, Osaka University, 2-8, Yamadaoka, Suita, Osaka 565-0871, Japan
Author to whom correspondence should be addressed.
Received: 19 October 2017 / Revised: 20 November 2017 / Accepted: 23 November 2017 / Published: 27 November 2017
View Full-Text   |   Download PDF [11928 KB, uploaded 27 November 2017]   |  


A thermal camera captures the temperature distribution of a scene as a thermal image. In thermal images, facial appearances of different people under different lighting conditions are similar. This is because facial temperature distribution is generally constant and not affected by lighting condition. This similarity in face appearances is advantageous for face detection. To detect faces in thermal images, cascade classifiers with Haar-like features are generally used. However, there are few studies exploring the local features for face detection in thermal images. In this paper, we introduce two approaches relying on local features for face detection in thermal images. First, we create new feature types by extending Multi-Block LBP. We consider a margin around the reference and the generally constant distribution of facial temperature. In this way, we make the features more robust to image noise and more effective for face detection in thermal images. Second, we propose an AdaBoost-based training method to get cascade classifiers with multiple types of local features. These feature types have different advantages. In this way we enhance the description power of local features. We did a hold-out validation experiment and a field experiment. In the hold-out validation experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females. For each participant, we captured 420 images with 10 variations in camera distance, 21 poses, and 2 appearances (participant with/without glasses). We compared the performance of cascade classifiers trained by different sets of the features. The experiment results showed that the proposed approaches effectively improve the performance of face detection in thermal images. In the field experiment, we compared the face detection performance in realistic scenes using thermal and RGB images, and gave discussion based on the results. View Full-Text
Keywords: thermal image; face detection; mixed features; haar-like; histogram of oriented gradient; local binary pattern; local ternary pattern; AdaBoost thermal image; face detection; mixed features; haar-like; histogram of oriented gradient; local binary pattern; local ternary pattern; AdaBoost

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ma, C.; Trung, N.T.; Uchiyama, H.; Nagahara, H.; Shimada, A.; Taniguchi, R.-I. Adapting Local Features for Face Detection in Thermal Image. Sensors 2017, 17, 2741.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top