Development of Machine-Learning-Based Facial Thermal Image Analysis for Dynamic Emotion Sensing.

ABSTRACT: Information on the relationship between facial thermal responses and emotional state is valuable for sensing emotion. Yet, previous research has typically relied on linear methods of analysis based on regions of interest (ROIs), which may overlook nonlinear pixel-wise information across the face. To address this limitation, we investigated the use of machine learning (ML) for pixel-level analysis of facial thermal images to estimate dynamic emotional arousal ratings. We collected facial thermal data from 20 participants who viewed five emotion-eliciting films and assessed their dynamic emotional self-reports. Our ML models, including random forest regression, support vector regression, ResNet-18, and ResNet-34, consistently demonstrated superior estimation performance compared to tradition

SUBMITTER: Tang B 

PROVIDER: S-EPMC12431138 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature