Proposal of an Image Processing Method for Enhancing Robustness in Stress Coping Style Classification Using <scp>TFI</scp>

Abstract Human stress coping styles, classified as active or passive, can be distinguished through hemodynamic measurements. However, conventional cuff‐based methods are impractical for daily use. Previous studies have explored thermal facial imaging (TFI) as a non‐contact alternative and developed classification models for stress coping styles. However, challenges such as facial misalignment and noise from muscle activity have led to a decrease in classification accuracy. This study proposes an image processing method to reduce these sources of noise and enhance model robustness. As a result, our approach improved classification accuracy by approximately 10% compared to conventional methods. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Authors

Institutions

Publication Details

Journal
IEEJ Transactions on Electrical and Electronic Engineering
Published
2025-09-29
DOI
https://doi.org/10.1002/tee.70167
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proposal of an Image Processing Method for Enhancing Robustness in Stress Coping Style Classification Using TFI

Kent Nagumo, Akio Nozawa, T. Okajima, Kosuke Kuzuma
IEEJ Transactions on Electrical and Electronic Engineering
Emotion and Mood Recognition
article

Proposal of an Image Processing Method for Enhancing Robustness in Stress Coping Style Classification Using TFI

Kent Nagumo, Akio Nozawa, T. Okajima, Kosuke Kuzuma
article en

Abstract

Abstract Human stress coping styles, classified as active or passive, can be distinguished through hemodynamic measurements. However, conventional cuff‐based methods are impractical for daily use. Previous studies have explored thermal facial imaging (TFI) as a non‐contact alternative and developed classification models for stress coping styles. However, challenges such as facial misalignment and noise from muscle activity have led to a decrease in classification accuracy. This study proposes an image processing method to reduce these sources of noise and enhance model robustness. As a result, our approach improved classification accuracy by approximately 10% compared to conventional methods. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

IEEJ Transactions on Electrical and Electronic Engineering
Aoyama Gakuin University (JP)
Openalex Percentile: Top 72%
Emotion and Mood Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.