Facial Expression Based Emotion Recognition
Human communication predominantly relies on spoken and written language; however, nonverbal cues, such as facial expressions, play a critical role in conveying emotions. This study details the development and evaluation of a deep learning model for Facial Emotion Recognition (FER) utilizing the VGG-...
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| Format: | Article |
| Language: | Indonesian |
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LP3M Universitas Nurul Jadid
2025-04-01
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| Series: | Journal of Electrical Engineering and Computer |
| Subjects: | |
| Online Access: | https://ejournal.unuja.ac.id/index.php/jeecom/article/view/11069 |
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| _version_ | 1849328522543235072 |
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| author | Muhammad Ibrahim Burhan Ergen |
| author_facet | Muhammad Ibrahim Burhan Ergen |
| author_sort | Muhammad Ibrahim |
| collection | DOAJ |
| description | Human communication predominantly relies on spoken and written language; however, nonverbal cues, such as facial expressions, play a critical role in conveying emotions. This study details the development and evaluation of a deep learning model for Facial Emotion Recognition (FER) utilizing the VGG-16 architecture and the FER2013 dataset which includes over 35,000 facial images taken in natural settings, depicting seven emotions. The objective was to enhance recognition, accuracy and performance beyond the existing benchmarks in the literature. Transfer learning was employed by leveraging pre-trained VGG-16 weights, with the classification layers restructured and fine-tuned for emotion categorization. Comprehensive preprocessing, including normalization and data augmentation, was implemented to improve the model generalization and mitigate overfitting. The final model achieved an accuracy of 85.77%, surpassing several previous VGG-16-based FER models. The model performance was assessed using metrics such as accuracy, precision, recall, and F1-score, confirming the model's reliability. Integral to this success was the incorporation of hyperparameter tuning and regularization techniques, notably, dropout and early stopping. The model demonstrated the capability to extract salient features from low-resolution images, thereby supporting its robustness. Additionally,the potential use cases of the model in areas such as transportation safety, security systems, and customer interaction analysis can address in the Future study to enhance the model's real-world applicability by utilizing more diverse datasets and advanced architectures |
| format | Article |
| id | doaj-art-b21bbc0b1b8d4ab3a4cb4882fb520c20 |
| institution | Kabale University |
| issn | 2715-0410 2715-6427 |
| language | Indonesian |
| publishDate | 2025-04-01 |
| publisher | LP3M Universitas Nurul Jadid |
| record_format | Article |
| series | Journal of Electrical Engineering and Computer |
| spelling | doaj-art-b21bbc0b1b8d4ab3a4cb4882fb520c202025-08-20T03:47:33ZindLP3M Universitas Nurul JadidJournal of Electrical Engineering and Computer2715-04102715-64272025-04-017116617410.33650/jeecom.v7i1.110693886Facial Expression Based Emotion RecognitionMuhammad Ibrahim0Burhan Ergen1Firat UniversityFirat UniversityHuman communication predominantly relies on spoken and written language; however, nonverbal cues, such as facial expressions, play a critical role in conveying emotions. This study details the development and evaluation of a deep learning model for Facial Emotion Recognition (FER) utilizing the VGG-16 architecture and the FER2013 dataset which includes over 35,000 facial images taken in natural settings, depicting seven emotions. The objective was to enhance recognition, accuracy and performance beyond the existing benchmarks in the literature. Transfer learning was employed by leveraging pre-trained VGG-16 weights, with the classification layers restructured and fine-tuned for emotion categorization. Comprehensive preprocessing, including normalization and data augmentation, was implemented to improve the model generalization and mitigate overfitting. The final model achieved an accuracy of 85.77%, surpassing several previous VGG-16-based FER models. The model performance was assessed using metrics such as accuracy, precision, recall, and F1-score, confirming the model's reliability. Integral to this success was the incorporation of hyperparameter tuning and regularization techniques, notably, dropout and early stopping. The model demonstrated the capability to extract salient features from low-resolution images, thereby supporting its robustness. Additionally,the potential use cases of the model in areas such as transportation safety, security systems, and customer interaction analysis can address in the Future study to enhance the model's real-world applicability by utilizing more diverse datasets and advanced architectureshttps://ejournal.unuja.ac.id/index.php/jeecom/article/view/11069facialemotion recognition(fer), fer2013 dataset vgg16 emotion analysis deep learning |
| spellingShingle | Muhammad Ibrahim Burhan Ergen Facial Expression Based Emotion Recognition Journal of Electrical Engineering and Computer facialemotion recognition(fer), fer2013 dataset vgg16 emotion analysis deep learning |
| title | Facial Expression Based Emotion Recognition |
| title_full | Facial Expression Based Emotion Recognition |
| title_fullStr | Facial Expression Based Emotion Recognition |
| title_full_unstemmed | Facial Expression Based Emotion Recognition |
| title_short | Facial Expression Based Emotion Recognition |
| title_sort | facial expression based emotion recognition |
| topic | facialemotion recognition(fer), fer2013 dataset vgg16 emotion analysis deep learning |
| url | https://ejournal.unuja.ac.id/index.php/jeecom/article/view/11069 |
| work_keys_str_mv | AT muhammadibrahim facialexpressionbasedemotionrecognition AT burhanergen facialexpressionbasedemotionrecognition |