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 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.