Citation

Chang, Jia Xiu and Lee, Chin Poo and Lim, Kian Ming and Lim, Jit Yan (2023) Facial Expression Recognition with Machine Learning. In: 2023 11th International Conference on Information and Communication Technology (ICoICT), 23-24 August 2023, Melaka, Malaysia.

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Facial expression recognition, Convolutional Neural Network (CNN), Vision transformers, Ensemble models
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 07:54
Last Modified:	31 Oct 2023 07:54
URII:	http://shdl.mmu.edu.my/id/eprint/11794

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