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Related Experiment Video

Updated: Jul 17, 2026

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

Published on: May 5, 2011

Facial expression recognition using thermal image.

Guotai Jiang¹, Xuemin Song, Fuhui Zheng

¹School of Life Science and Technology, Tongji University, Shanghai, China. jianggt@mail.tongji.edu.cn.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|February 7, 2007

Summary

This study uses Infrared Thermal Imaging (IRTI) and mathematical morphology to recognize facial expressions. IRTI effectively captures distinct geometric and temperature changes associated with different expressions, proving its feasibility for real-time monitoring.

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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Area of Science:

Computer Vision
Biomedical Engineering
Human-Computer Interaction

Background:

Facial expression recognition is crucial for understanding human emotions and social cues.
Traditional methods often rely on visible light imaging, which can be affected by illumination conditions.
Infrared Thermal Imaging (IRTI) offers an alternative by capturing subtle temperature variations related to physiological changes.

Purpose of the Study:

To investigate the feasibility of facial expression recognition using Infrared Thermal Imaging (IRTI).
To analyze geometric and thermal characteristics of facial regions for expression identification.
To explore the potential of IRTI-based methods for real-time monitoring and medical applications.

Main Methods:

Application of mathematical morphology techniques to analyze Infrared Thermal Images.
Extraction and analysis of geometric features from specific facial regions of interest.
Correlation of identified geometric characteristics and facial temperature changes with different expressions.

Main Results:

Distinct differences in geometric characteristics were observed in the regions of interest for various facial expressions.
Facial temperature changes were found to correlate consistently with specific expressions.
The study demonstrated the feasibility of facial expression recognition based on IRTI data.

Conclusions:

Infrared Thermal Imaging (IRTI) combined with mathematical morphology is a viable approach for facial expression recognition.
This method provides real-time facial expression monitoring capabilities.
Potential applications include auxiliary diagnosis and medical monitoring for diseases.