An Ensemble Learning Approach for Facial Emotion Recognition Based on Deep Learning Techniques
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| Pubblicato in: | Electronics vol. 14, no. 17 (2025), p. 3415-3445 |
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MDPI AG
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| Accesso online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14173415 |2 doi | |
| 035 | |a 3249684517 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Almubarak Manal | |
| 245 | 1 | |a An Ensemble Learning Approach for Facial Emotion Recognition Based on Deep Learning Techniques | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Facial emotion recognition (FER) is an evolving sub-field of computer vision and affective computing. It entails the development of algorithms and models to detect, analyze, and interpret facial expressions, thereby determining individuals’ emotional states. This paper explores the effectiveness of transfer learning using the EfficientNet-B0 convolutional neural network for FER, alongside the utilization of stacking techniques. The pretrained EfficientNet-B0 model is employed to train on a dataset comprising a diverse range of natural human face images for emotion recognition. This dataset consists of grayscale images categorized into eight distinct emotion classes. Our approach involves fine-tuning the pretrained EfficientNet-B0 model, adapting its weights and layers to capture subtle facial expressions. Moreover, this study utilizes ensemble learning by integrating transfer learning from pretrained models, a strategic tuning approach, binary classifiers, and a meta-classifier. Our approach achieves superior performance in accurately identifying and classifying emotions within facial images. Experimental results for the meta-classifier demonstrate 100% accuracy on the test set. For further assessment, we also train our meta-classifier on a Cohn–Kanade (CK+) dataset, achieving 92% accuracy on the test set. These findings highlight the effectiveness and potential of employing transfer learning and stacking techniques with EfficientNet-B0 for FER tasks. | |
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Emotional factors | ||
| 653 | |a Affective computing | ||
| 653 | |a Test sets | ||
| 653 | |a Emotion recognition | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Effectiveness | ||
| 653 | |a Images | ||
| 653 | |a Emotions | ||
| 653 | |a Computer vision | ||
| 653 | |a Automation | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Ensemble learning | ||
| 700 | 1 | |a Alsulaiman, Fawaz A | |
| 773 | 0 | |t Electronics |g vol. 14, no. 17 (2025), p. 3415-3445 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3249684517/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3249684517/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3249684517/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |