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Título del libro:
Título del capítulo: Unsupervised Thermal Pattern Analysis of Facial Regions During the Trier Social Stress Test: An Exploratory Approach

Autores UNAM:
ERIK LEONARDO MATEOS SALGADO;

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Autores externos:

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Idioma:

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Año de publicación:
2026

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Palabras clave:

Facial regions; Hier-archical clustering; Hierarchical Clustering; Pattern analysis; Response patterns; Stress test; Thermal patterns; Thermal response; Thermography; Trier social stress test

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Resumen:

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.Stress affects a significant proportion of the world?s population, making it a crucial topic to study. This work explores the use of facial thermographic imaging to automatically identify thermal response patterns associated with stress. Ten university students underwent a short Trier Social Stress Test (TSST), during which infrared thermal images were captured. Subsequently, the temperature was extracted from key regions of interest (ROIs) in the facial area, including the forehead, tip of the nose, cheeks and the maxilla. A hierarchical clustering model method was then applied, using the histogram from these regions as feature sets to group participants according to their physiological thermal responses. The Ward linkage method was applied to compute the pairwise distances and generate a dendrogram that visually and quantitatively grouped participants according to similarities in their thermal response profiles. The results show a trend towards a mind-body relationship, however, the correlation between clustering and self-reported stress levels was not statistically significant (p = 0.166). Nevertheless, the cluster structure revealed physiologically significant clusters, suggesting inter-individual differences in thermal responses to stress. These results demonstrate the potential of unsupervised learning methods as a means of generating labels based on physiological data rather than subjective perception. This reduces labelling bias and provides an additional approach to stress detection in affective computing and psychophysiological research.

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Entidades citadas de la UNAM:

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Fuente:


ISBN:
9783032149282
Editorial:
Springer Science and Business Media Deutschland GmbH

Tipo de producto:
Conference Paper

DOI:
10.1007/978-3-032-14929-9_6