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Related Experiment Video

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

Adapting Local Features for Face Detection in Thermal Image.

Chao Ma¹, Ngo Thanh Trung², Hideaki Uchiyama³

¹Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan. ma@limu.ait.kyushu-u.ac.jp.

Sensors (Basel, Switzerland)

|December 1, 2017

Summary

This study introduces novel local features for enhanced face detection in thermal images, improving accuracy and robustness. The new methods outperform traditional approaches in both controlled and real-world scenarios.

Keywords:

AdaBoost face detection haar-like histogram of oriented gradient local binary pattern local ternary pattern mixed features thermal image

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Last Updated: Feb 17, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Published on: May 5, 2011

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

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Published on: August 30, 2013

Area of Science:

Computer Vision
Machine Learning
Biomedical Imaging

Background:

Thermal images offer consistent facial appearance regardless of lighting conditions, aiding face detection.
Traditional face detection in thermal images often uses Haar-like features, with limited exploration of local features.

Purpose of the Study:

To develop and evaluate new local feature-based approaches for improved face detection in thermal images.
To enhance the robustness and descriptive power of features for thermal face detection.

Main Methods:

Extended Multi-Block Local Binary Patterns (LBP) to create robust local features, incorporating facial temperature distribution.
Developed an AdaBoost-based training method for cascade classifiers using diverse local feature types.

Main Results:

Proposed methods demonstrated significant improvements in face detection performance in thermal images.
Experiments validated the effectiveness of the new feature types and AdaBoost training in controlled and field settings.

Conclusions:

The novel local feature approaches enhance face detection accuracy and reliability in thermal imaging.
These findings contribute to advancing face recognition technologies in challenging thermal imaging environments.