Static Facial Expression Recognition in the wild: Taxonomy, trends and challenges

Jing-Zhi Koay and Jason Teo (2025) Static Facial Expression Recognition in the wild: Taxonomy, trends and challenges. International Journal of Machine Intelligence and Computing, 1 (2). pp. 1-25.

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Abstract

In recent years, Facial Expression Recognition (FER) has gained significant at-tention due to its wide application and potential in various domains. FER is the research field that focuses on recognizing and classifying human emotions expressed by humans into emo-tion categories using computer vision. Different machine learning techniques have been applied to this research field with promising outcomes through the application of increasingly more powerful machine learning algorithms. This systematic literature review is conducted to investigate static FER on unconstrained datasets. A total of 32 studies were retrieved from four major academic repositories. The aim of this study is to provide a comprehensive review of static FER research on unconstrained facial expression image datasets including the overview of key concepts, the approaches applied, the datasets used, the current state-of-the-art as well as the future directions of research in this fast-developing research field. Deep learning methods emerged as the most promising approach for static FER while second-order pooling in CNN allowed for improved representation of regional features and facial landmark distortion.

Item Type:	Article
Keyword:	Emotion recognition, facial recognition, machine learning, computer vision, affective computing
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q300-390 Cybernetics T Technology > TJ Mechanical engineering and machinery > TJ1-1570 Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	JUNAINE JASNI -
Date Deposited:	13 Aug 2025 12:16
Last Modified:	13 Aug 2025 12:16
URI:	https://eprints.ums.edu.my/id/eprint/44865

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