Weighted Ensemble Based on Prisoner Dilemma for Facial Expression Recognition
Facial Expression Recognition (FER) is a task that recognizes the expression or emotion of a person based on their face, enabling computers to identify the mood and emotions of individuals. FER tasks in real-world scenarios remain challenging due to the variations of many parameters captured by the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11048882/ |
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| Summary: | Facial Expression Recognition (FER) is a task that recognizes the expression or emotion of a person based on their face, enabling computers to identify the mood and emotions of individuals. FER tasks in real-world scenarios remain challenging due to the variations of many parameters captured by the image sensor. Many approaches have been proposed to improve FER tasks in real-world scenarios. One of them is the utilization of an ensemble model. The weighted ensemble is one way to do an ensemble by weighting each model with a weight. However, the weight values are left to the researcher to decide, which raises another problem in determining a weight for the weighted ensemble. In this research, we proposed a novel weighting voting inspired by the Prisoner Dilemma. Based on the experiments, our proposed method achieved an accuracy and f1 score of 83.25% and 75.02% respectively in the RAFDB dataset, and an accuracy and f1 score of 64.73% and 63.07% in the FER2013 dataset, which is relatively better than the other state-of-the-art and our baseline. |
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| ISSN: | 2169-3536 |