|

Review of methods for classifying human emotions for emotion recognition purposes

Authors: Rusanova E.G.
Published in issue: #8(73)/2022
DOI: 10.18698/2541-8009-2022-8-821


Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing, Statistics

Keywords: emotions, emotion classification, emotion recognition, spontaneous emotions, static emotions, automatic classifiers, computer vision, intelligent systems, machine learning
Published: 07.10.2022

An important element of social interaction is a person's ability to determine what emotions other people are experiencing. In this paper a review of existing classifications of human emotions was carried out and their main differences and peculiarities were considered. A literature review of approaches explaining what human emotions are and the reasons for them was performed, the leading at the moment programs for recognizing emotions from a human face were considered and their comparative analysis was implemented. Conclusions are made about the need to consider not only the basic human emotions, but also affects such as interest, pain, boredom, frustration, etc. With an increase in the number of recognizable emotion categories, automated methods can overcome their current limitation of classifying a small set of emotion labels, which are insufficient to describe complex, sometimes expressive human behavior.


References

[1] Ekman P. An argument for basic emotions. Cogn. Emot., 1992, vol. 6, no. 3/4, pp. 169–200. DOI: https://doi.org/10.1080/02699939208411068

[2] Izard C.E. The psychology of emotions. Plenum Press, 1991. (Russ. ed.: Psikhologiya emotsiy. Sankt-Petersburg, Piter Publ., 2008.)

[3] Dodonov B.I. Emotsiya kak tsennost [Emotion as a treasure]. Moscow, Politizdat Publ., 1978 (in Russ.).

[4] Leontyev A.N. Lektsii po obshchey psikhologii [Lectures on general psychology]. Moscow, Akademiya Publ., 2010 (in Russ.).

[5] Rubinshteyn S.L. Osnovy obshchey psikhologii [Basics of general psychology]. Sankt-Petersburg, Piter Publ., 1999 (in Russ.).

[6] Vundt V. Ocherk psikhologii [Psychology sketch]. Moscow, Terra Publ., 2015 (in Russ.).

[7] Dupre D., Krumhuber E.G., Küster D. et al. A performance comparison of eight commercially available automatic classifiers for facial affect recognition. PLoS ONE, 2020, vol. 15, no. 4, art. e0231968. DOI: https://doi.org/10.1371/journal.pone.0231968

[8] Yin L., Chen X., Sun Y. et al. A high-resolution 3D dynamic facial expression database. Proceedings of the international conference on automatic face and gesture recognition. 8th IEEE Int. Conf. on Automatic Face & Gesture Recognition, 2008. DOI: https://doi.org/10.1109/AFGR.2008.4813324

[9] O’Toole A.J., Harms J., Snow S.L. et al. A video database of moving faces and people. IEEE Trans. Pattern Anal. Mach. Intell., 2005, vol. 27, no. 5, pp. 812–816. DOI: https://doi.org/10.1109/TPAMI.2005.90

[10] Krumhuber E.G., Skora L., Küster D. et al. A review of dynamic datasets for facial expression research. Emot. Rev., 2017, vol. 9, no. 3, pp. 280–292. DOI: https://doi.org/10.1177/1754073916670022