Citation

Cheah, Tuck Feng and Lee, Chin Poo and Lim, Kian Ming and Lim, Jit Yan (2023) Facial Expression Classification with Deep Learning: A Comparative Study. In: 2023 IEEE 11th Conference on Systems, Process & Control (ICSPC), 16-16 December 2023, Malacca, Malaysia.

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Abstract

Facial expression recognition is a significant area of research in computer vision with diverse applications. One of its primary challenges lies in the variations of facial expressions among individuals, cultures, and contexts. Various techniques, such as Convolutional Neural Networks and Vision Transformers, have emerged to address this challenge. This paper aims to compare the performance of five state-of-the-art models: VGG-19, EfficientNet-B7, Vision Transformer, Dataefficient Image Transformers, and Co-scale conv-attentional image Transformers, on two facial expression datasets: FER+ and CK+. The paper also provides an analysis in terms of strengths, weaknesses, and the factors affecting the performance.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Facial Expression, Facial Expression Recognition, Convolution Neural Network, CNN, Vision Transformer
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:	27 Mar 2024 00:52
Last Modified:	27 Mar 2024 00:52
URII:	http://shdl.mmu.edu.my/id/eprint/12196

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