Designing an Efficient System for Emotion Recognition Using CNN
Implementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This techniqu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2023/9351345 |
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| _version_ | 1832559553578795008 |
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| author | Donia Ammous Achraf Chabbouh Awatef Edhib Ahmed Chaari Fahmi Kammoun Nouri Masmoudi |
| author_facet | Donia Ammous Achraf Chabbouh Awatef Edhib Ahmed Chaari Fahmi Kammoun Nouri Masmoudi |
| author_sort | Donia Ammous |
| collection | DOAJ |
| description | Implementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This technique is used to analyse various sentiments and reveal a person’s behavior. It could be related to the mental or physiological state of mind. This paper mainly focuses on a human emotion recognition system through a detected human face. Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance. |
| format | Article |
| id | doaj-art-5248f220a81e426abca5900ecc0f22f3 |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-5248f220a81e426abca5900ecc0f22f32025-02-03T01:29:49ZengWileyJournal of Electrical and Computer Engineering2090-01552023-01-01202310.1155/2023/9351345Designing an Efficient System for Emotion Recognition Using CNNDonia Ammous0Achraf Chabbouh1Awatef Edhib2Ahmed Chaari3Fahmi Kammoun4Nouri Masmoudi5National School of Engineers of SfaxHigher Institute of Technological Studies of Sidi BouzidSogimel: A Consulting Company in Computer Engineering and Video SurveillanceAnavid FranceNational School of Engineers of SfaxNational School of Engineers of SfaxImplementing an efficient system for emotion recognition has recently posed a challenge that has not been fully developed yet. Facial emotion recognition (FER) is an important subject matter in the fields of artificial intelligence (AI) since it exhibits a greater commercial potential. This technique is used to analyse various sentiments and reveal a person’s behavior. It could be related to the mental or physiological state of mind. This paper mainly focuses on a human emotion recognition system through a detected human face. Its accuracy was improved via different data augmentation tools, early stopping, and generative adversarial networks (GANs). Compared to previous methods, experimental results show that the proposed method provides a 0.55% to 35.7% gain performance.http://dx.doi.org/10.1155/2023/9351345 |
| spellingShingle | Donia Ammous Achraf Chabbouh Awatef Edhib Ahmed Chaari Fahmi Kammoun Nouri Masmoudi Designing an Efficient System for Emotion Recognition Using CNN Journal of Electrical and Computer Engineering |
| title | Designing an Efficient System for Emotion Recognition Using CNN |
| title_full | Designing an Efficient System for Emotion Recognition Using CNN |
| title_fullStr | Designing an Efficient System for Emotion Recognition Using CNN |
| title_full_unstemmed | Designing an Efficient System for Emotion Recognition Using CNN |
| title_short | Designing an Efficient System for Emotion Recognition Using CNN |
| title_sort | designing an efficient system for emotion recognition using cnn |
| url | http://dx.doi.org/10.1155/2023/9351345 |
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