Facial Expression Recognition with Machine Learning

Human facial expressions play a crucial role in communication and enhancing interactions between humans and computers. This paper presents a novel approach for facial expression recognition using an ensemble classifier consisting of pre-trained models and vision transformers. The ensemble classifier comprises four models: VGG-19, VGGFace, ViT-B/16, and ViT-B/32. To evaluate the performance, the ensemble classifiers employ hard majority voting on three widely-used public datasets: CK+, FER2013, and JAFFE. The experimental results demonstrate that our proposed ensemble classifiers surpass the state-of-the-art methods across all datasets. Notably, we achieve outstanding accuracy rates, reaching 100% accuracy on the cleaned CK+ dataset, 76.30% accuracy on the cleaned FER-2013 dataset, and 100% accuracy on the cleaned JAFFE dataset.