Facial Expression Classification with Deep Learning: A Comparative Study

Tuck Feng Cheah; Chin Poo Lee; Kian Ming Lim; Jit Yan Lim

doi:10.1109/ICSPC59664.2023.10420210

Facial Expression Classification with Deep Learning: A Comparative Study

Tuck Feng Cheah, Chin Poo Lee, Kian Ming Lim, Jit Yan Lim

Multimedia University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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, Data-efficient 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.

Original language	English
Title of host publication	2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	56-59
Number of pages	4
ISBN (Electronic)	9798350340860
DOIs	https://doi.org/10.1109/ICSPC59664.2023.10420210
Publication status	Published - 2023
Externally published	Yes
Event	11th IEEE Conference on Systems, Process and Control, ICSPC 2023 - Malacca, Malaysia Duration: 16 Dec 2023 → …

Publication series

Name	2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings

Conference

Conference	11th IEEE Conference on Systems, Process and Control, ICSPC 2023
Country/Territory	Malaysia
City	Malacca
Period	16/12/23 → …

Keywords

CNN
Convolution Neural Network
Facial Expression
Facial Expression Recognition
Vision Transformer

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10.1109/ICSPC59664.2023.10420210

Cite this

Cheah, T. F., Lee, C. P., Lim, K. M., & Lim, J. Y. (2023). Facial Expression Classification with Deep Learning: A Comparative Study. In 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings (pp. 56-59). (2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICSPC59664.2023.10420210

Cheah, Tuck Feng ; Lee, Chin Poo ; Lim, Kian Ming et al. / Facial Expression Classification with Deep Learning: A Comparative Study. 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 56-59 (2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings).

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title = "Facial Expression Classification with Deep Learning: A Comparative Study",

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, Data-efficient 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.",

keywords = "CNN, Convolution Neural Network, Facial Expression, Facial Expression Recognition, Vision Transformer",

author = "Cheah, {Tuck Feng} and Lee, {Chin Poo} and Lim, {Kian Ming} and Lim, {Jit Yan}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th IEEE Conference on Systems, Process and Control, ICSPC 2023 ; Conference date: 16-12-2023",

year = "2023",

doi = "10.1109/ICSPC59664.2023.10420210",

language = "English",

series = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "56--59",

booktitle = "2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings",

}

Cheah, TF, Lee, CP, Lim, KM & Lim, JY 2023, Facial Expression Classification with Deep Learning: A Comparative Study. in 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings. 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 56-59, 11th IEEE Conference on Systems, Process and Control, ICSPC 2023, Malacca, Malaysia, 16/12/23. https://doi.org/10.1109/ICSPC59664.2023.10420210

Facial Expression Classification with Deep Learning: A Comparative Study. / Cheah, Tuck Feng; Lee, Chin Poo; Lim, Kian Ming et al.
2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 56-59 (2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Facial Expression Classification with Deep Learning: A Comparative Study

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AU - Lee, Chin Poo

AU - Lim, Kian Ming

AU - Lim, Jit Yan

PY - 2023

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N2 - 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, Data-efficient 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.

AB - 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, Data-efficient 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.

KW - CNN

KW - Convolution Neural Network

KW - Facial Expression

KW - Facial Expression Recognition

KW - Vision Transformer

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DO - 10.1109/ICSPC59664.2023.10420210

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T3 - 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings

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BT - 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings

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Cheah TF, Lee CP, Lim KM, Lim JY. Facial Expression Classification with Deep Learning: A Comparative Study. In 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 56-59. (2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings). doi: 10.1109/ICSPC59664.2023.10420210

Facial Expression Classification with Deep Learning: A Comparative Study

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