Vol. 17, No. 9, September 30, 2023
10.3837/tiis.2023.09.011,
Download Paper (Free):
Abstract
The field of facial expression recognition (FER) has been actively researched to improve
human-computer interaction. In recent years, deep learning techniques have gained popularity
for addressing FER, with numerous studies proposing end-to-end frameworks that stack or
widen significant convolutional neural network layers. While this has led to improved
performance, it has also resulted in larger model sizes and longer inference times. To overcome
this challenge, our work introduces a novel lightweight model architecture. The architecture
incorporates three key factors: Depth-wise Separable Convolution, Residual Block, and
Attention Modules. By doing so, we aim to strike a balance between model size, inference
speed, and accuracy in FER tasks. Through extensive experimentation on popular benchmark
FER datasets, our proposed method has demonstrated promising results. Notably, it stands out
due to its substantial reduction in parameter count and faster inference time, while maintaining
accuracy levels comparable to other lightweight models discussed in the existing literature.
Statistics
Show / Hide Statistics
Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.
Cite this article
[IEEE Style]
H. Dinh, H. Do, T. Doan, C. Le, N. X. Bach, T. M. Phuong, V. Vu, "FGW-FER: Lightweight Facial Expression Recognition with Attention," KSII Transactions on Internet and Information Systems, vol. 17, no. 9, pp. 2505-2528, 2023. DOI: 10.3837/tiis.2023.09.011.
[ACM Style]
Huy-Hoang Dinh, Hong-Quan Do, Trung-Tung Doan, Cuong Le, Ngo Xuan Bach, Tu Minh Phuong, and Viet-Vu Vu. 2023. FGW-FER: Lightweight Facial Expression Recognition with Attention. KSII Transactions on Internet and Information Systems, 17, 9, (2023), 2505-2528. DOI: 10.3837/tiis.2023.09.011.
[BibTeX Style]
@article{tiis:56000, title="FGW-FER: Lightweight Facial Expression Recognition with Attention", author="Huy-Hoang Dinh and Hong-Quan Do and Trung-Tung Doan and Cuong Le and Ngo Xuan Bach and Tu Minh Phuong and Viet-Vu Vu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2023.09.011}, volume={17}, number={9}, year="2023", month={September}, pages={2505-2528}}