What not to do in facial infrared thermographic measurements: A post data enhancement

Matthew Pike, Siegfried Yeboah, Xianyun Fu

Research output: Journal PublicationArticlepeer-review

Abstract

The accuracy of infrared thermographic measurements depends on several factors, including movement of target. In this study, accuracy of nose tip temperatures obtained in a mental workload assessment using a thermal imaging camera were impacted by participants’ movement and camera zooming/panning. To correct these measurement errors, we compared manual facial landmark identification techniques using data labelling software with an automated deep learning-based approach utilised for facial landmark tracking and evaluated both against the built-in tracking features of the thermal camera, Thermal Spot Tracking. Using the Manual Thermal Landmark Annotation measurements as the ground truth, our results show that the Automated Facial Feature Tracking approach, which is the AI based approach performed better than the Thermal Spot Tracking as it matched comparatively more spatial coordinates and temperature datapoints as well as showed comparatively lower mean relative error. The study highlights the potential of AI in enhancing the accuracy of thermographic measurements, particularly in applications involving facial temperature analysis.

Original languageEnglish
Article number109027
JournalEngineering Applications of Artificial Intelligence
Volume136
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Convolutional neural network
  • Deep learning
  • Facial landmark detection
  • Infrared thermography

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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