@inproceedings{cd099db4c26944d0baf72cfdc7393cce,
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",
address = "United States",
}