On Facial Expression Recognition Benchmarks
Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus,...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/9917246 |
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| author | Ebenezer Owusu Jacqueline Asor Kumi Justice Kwame Appati |
| author_facet | Ebenezer Owusu Jacqueline Asor Kumi Justice Kwame Appati |
| author_sort | Ebenezer Owusu |
| collection | DOAJ |
| description | Facial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus, 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. This 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. The 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. Therefore, 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. |
| format | Article |
| id | doaj-art-f1f7f8139bc641b9a0b1fe541db5bed4 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-f1f7f8139bc641b9a0b1fe541db5bed42025-02-03T01:24:48ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/99172469917246On Facial Expression Recognition BenchmarksEbenezer Owusu0Jacqueline Asor Kumi1Justice Kwame Appati2Department of Computer Science, University of Ghana, Accra, GhanaDepartment of Computer Science, University of Ghana, Accra, GhanaDepartment of Computer Science, University of Ghana, Accra, GhanaFacial expression is an important form of nonverbal communication, as it is noted that 55% of what humans communicate is expressed in facial expressions. There are several applications of facial expressions in diverse fields including medicine, security, gaming, and even business enterprises. Thus, 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. This 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. The 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. Therefore, 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.http://dx.doi.org/10.1155/2021/9917246 |
| spellingShingle | Ebenezer Owusu Jacqueline Asor Kumi Justice Kwame Appati On Facial Expression Recognition Benchmarks Applied Computational Intelligence and Soft Computing |
| title | On Facial Expression Recognition Benchmarks |
| title_full | On Facial Expression Recognition Benchmarks |
| title_fullStr | On Facial Expression Recognition Benchmarks |
| title_full_unstemmed | On Facial Expression Recognition Benchmarks |
| title_short | On Facial Expression Recognition Benchmarks |
| title_sort | on facial expression recognition benchmarks |
| url | http://dx.doi.org/10.1155/2021/9917246 |
| work_keys_str_mv | AT ebenezerowusu onfacialexpressionrecognitionbenchmarks AT jacquelineasorkumi onfacialexpressionrecognitionbenchmarks AT justicekwameappati onfacialexpressionrecognitionbenchmarks |