A survey on facial emotion recognition techniques: A state-of-the-art literature review

In this survey, a systematic literature review of the state-of-the-art on emotion expression recognition from facial images is presented. The paper has as main objective arise the most commonly used strategies employed to interpret and recognize facial emotion expressions, published over the past fe...

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Bibliographic Details
Published in:	Information sciences Vol. 582; pp. 593 - 617
Main Authors:	Canal, Felipe Zago, Müller, Tobias Rossi, Matias, Jhennifer Cristine, Scotton, Gustavo Gino, de Sa Junior, Antonio Reis, Pozzebon, Eliane, Sobieranski, Antonio Carlos
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01-01-2022
Subjects:	Emotion Recognition Facial emotion recognition Pattern recognition Systematic literature review Pattern recognition Facial emotion recognition Emotion Recognition Systematic literature review
Online Access:	Get full text
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Summary:	In this survey, a systematic literature review of the state-of-the-art on emotion expression recognition from facial images is presented. The paper has as main objective arise the most commonly used strategies employed to interpret and recognize facial emotion expressions, published over the past few years. For this purpose, a total of 51 papers were analyzed over the literature totaling 94 distinct methods, collected from well-established scientific databases (ACM Digital Library, IEEE Xplore, Science Direct and Scopus), whose works were categorized according to its main construction concept. From the analyzed works, it was possible to categorize them into two main trends: classical and those approaches specifically designed by the use of neural networks. The obtained statistical analysis demonstrated a marginally better recognition precision for the classical approaches when faced to neural networks counterpart, but with a reduced capacity of generalization. Additionally, the present study verified the most popular datasets for facial expression and emotion recognition showing the pros and cons each and, thereby, demonstrating a real demand for reliable data-sources regarding artificial and natural experimental environments.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2021.10.005