On Facial Expression Recognition Benchmarks
Date
2021
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi
Abstract
Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is
expressed in facial expressions. *ere are several applications of facial expressions in diverse fields including medicine, security,
gaming, and even business enterprises. *us, currently, automatic facial expression recognition is a hotbed research area that
attracts lots of grants and therefore the need to understand the trends very well. *is study, as a result, aims to review selected
published works in the domain of study and conduct valuable analysis to determine the most common and useful algorithms
employed in the study. We selected published works from 2010 to 2021 and extracted, analyzed, and summarized the findings
based on the most used techniques in feature extraction, feature selection, validation, databases, and classification. *e result of the
study indicates strongly that local binary pattern (LBP), principal component analysis (PCA), saturated vector machine (SVM),
CK+, and 10-fold cross-validation are the most widely used feature extraction, feature selection, classifier, database, and validation
method used, respectively. *erefore, in line with our findings, this study provides recommendations for research specifically for
new researchers with little or no background as to which methods they can employ and strive to improve.
Description
Research Article