Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)
The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last co...
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2023/2457898 |
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| _version_ | 1832553051019280384 |
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| author | Emmanuel Gbenga Dada David Opeoluwa Oyewola Stephen Bassi Joseph Onyeka Emebo Olugbenga Oluseun Oluwagbemi |
| author_facet | Emmanuel Gbenga Dada David Opeoluwa Oyewola Stephen Bassi Joseph Onyeka Emebo Olugbenga Oluseun Oluwagbemi |
| author_sort | Emmanuel Gbenga Dada |
| collection | DOAJ |
| description | The importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%. |
| format | Article |
| id | doaj-art-3eeb0614b9db4ee195003837a3cc1484 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3eeb0614b9db4ee195003837a3cc14842025-02-03T05:57:01ZengWileyApplied Computational Intelligence and Soft Computing1687-97322023-01-01202310.1155/2023/2457898Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10)Emmanuel Gbenga Dada0David Opeoluwa Oyewola1Stephen Bassi Joseph2Onyeka Emebo3Olugbenga Oluseun Oluwagbemi4Department of Mathematical SciencesDepartment of Mathematics and StatisticsDepartment of Computer EngineeringDepartment of Computer ScienceDepartment of Computer ScienceThe importance of facial expressions in nonverbal communication is significant because they help better represent the inner emotions of individuals. Emotions can depict the state of health and internal wellbeing of individuals. Facial expression detection has been a hot research topic in the last couple of years. The motivation for applying the convolutional neural network-10 (CNN-10) model for facial expression recognition stems from its ability to detect spatial features, manage translation invariance, understand expressive feature representations, gather global context, and achieve scalability, adaptability, and interoperability with transfer learning methods. This model offers a powerful instrument for reliably detecting and comprehending facial expressions, supporting usage in recognition of emotions, interaction between humans and computers, cognitive computing, and other areas. Earlier studies have developed different deep learning architectures to offer solutions to the challenge of facial expression recognition. Many of these studies have good performance on datasets of images taken under controlled conditions, but they fall short on more difficult datasets with more image diversity and incomplete faces. This paper applied CNN-10 and ViT models for facial emotion classification. The performance of the proposed models was compared with that of VGG19 and INCEPTIONV3. The CNN-10 outperformed the other models on the CK + dataset with a 99.9% accuracy score, FER-2013 with an accuracy of 84.3%, and JAFFE with an accuracy of 95.4%.http://dx.doi.org/10.1155/2023/2457898 |
| spellingShingle | Emmanuel Gbenga Dada David Opeoluwa Oyewola Stephen Bassi Joseph Onyeka Emebo Olugbenga Oluseun Oluwagbemi Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) Applied Computational Intelligence and Soft Computing |
| title | Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) |
| title_full | Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) |
| title_fullStr | Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) |
| title_full_unstemmed | Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) |
| title_short | Facial Emotion Recognition and Classification Using the Convolutional Neural Network-10 (CNN-10) |
| title_sort | facial emotion recognition and classification using the convolutional neural network 10 cnn 10 |
| url | http://dx.doi.org/10.1155/2023/2457898 |
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