Vol.37, No.1, 2021, pp.47-62, doi:10.32604/csse.2021.015222
Affective State Recognition Using Thermal-Based Imaging: A Survey
  • Mustafa M. M. Al Qudah, Ahmad S. A. Mohamed*, Syaheerah L. Lutfi
School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia
* Corresponding Author: Ahmad S. A. Mohamed. Email:
Received 10 November 2020; Accepted 20 December 2020; Issue published 05 February 2021
The thermal-based imaging technique has recently attracted the attention of researchers who are interested in the recognition of human affects due to its ability to measure the facial transient temperature, which is correlated with human affects and robustness against illumination changes. Therefore, studies have increasingly used the thermal imaging as a potential and supplemental solution to overcome the challenges of visual (RGB) imaging, such as the variation of light conditions and revealing original human affect. Moreover, the thermal-based imaging has shown promising results in the detection of psychophysiological signals, such as pulse rate and respiration rate in a contactless and noninvasive way. This paper presents a brief review on human affects and focuses on the advantages and challenges of the thermal imaging technique. In addition, this paper discusses the stages of thermal-based human affective state recognition, such as dataset type, preprocessing stage, region of interest (ROI), feature descriptors, and classification approaches with a brief performance analysis based on a number of works in the literature. This analysis could help beginners in the thermal imaging and affective recognition domain to explore numerous approaches used by researchers to construct an affective state system based on thermal imaging.
Thermal-based imaging; affective state recognition; spontaneous emotion; feature extraction and classification
Cite This Article
M. M., A. S. and S. L. Lutfi, "Affective state recognition using thermal-based imaging: a survey," Computer Systems Science and Engineering, vol. 37, no.1, pp. 47–62, 2021.
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