Open Access

REVIEW

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Darshan Babu L. Arasu¹, Ahmad Sufril Azlan Mohamed^1,*, Nur Intan Raihana Ruhaiyem¹, Nagaletchimee Annamalai², Syaheerah Lebai Lutfi¹, Mustafa M. Al Qudah¹

1 School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia
2 School of Distance Education, Universiti Sains Malaysia, Penang, 11800, Malaysia

* Corresponding Author:Ahmad Sufril Azlan Mohamed. Email:

(This article belongs to this Special Issue: Computer-Assisted Imaging Processing and Machine Learning Applications on Diagnosis of Chest Radiograph)

Computer Modeling in Engineering & Sciences 2022, 130(2), 633-652. https://doi.org/10.32604/cmes.2021.016985

Received 17 May 2021; Accepted 20 August 2021; Issue published 13 December 2021

Abstract

Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone’s life. Therefore, stress management is of vital importance in maintaining the psychological balance of a person. Thermal-based imaging technique is becoming popular among researchers due to its non-contact conductive nature. Moreover, thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Compared to other non-contact stress detection methods such as pupil dilation, keystroke behavior, social media interaction and voice modulation, thermal-based imaging provides better features with clear boundaries and requires no heavy methodology. This paper presented a brief review of previous work on thermal imaging related stress detection in humans. This paper also presented the stages of stress detection based on thermal face signatures such as dataset type, thermal image face detection, feature descriptors and classification performance comparisons are presented. This paper can help future researchers to understand stress detection based on thermal imaging by presenting the popular methods previous researchers use for stress detection based on thermal images.

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