Human Emotion Recognition: Review of Sensors and Methods
By Andrius Dzedzickis, Arturas Kaklauskas, Vytautas Bucinskas. Italian National Conference on Sensors, 2020
488 citations recorded by the source corpus.
Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.
Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/a2aad16321751269dc17e7d22702b228681d3211
How researchers see this paper
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In Cognitive Neuroscience
In Computer Vision and Pattern Recognition
- Chen Change Loy
- Ziwei Liu
- Wayne Wu
- Luc Van Gool
- Xiaogang Wang
- Ming-Hsuan Yang
- Dacheng Tao
- Andrea Vedaldi
In Experimental and Cognitive Psychology
Citations in the corpus
- The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise (2020, background): “In research laboratories, f R is among the signs that may help recognize and classify emotions, along with heart rate, heart rate variability, galvanic skin response, body temperature, body posture, and facial expressions [180,185,186].”
- CelebV-HQ: A Large-Scale Video Facial Attributes Dataset (2022, methodology): “, attribute recognition [97,17,41], action recognition [81,37], emotion recognition [18,47] and forgery detection [50,27,104]).”