التفاصيل البيبلوغرافية
العنوان: |
FGW-FER: Lightweight Facial Expression Recognition with Attention. |
المؤلفون: |
Huy-Hoang Dinh, Hong-Quan Do, Trung-Tung Doan, Cuong Le, Ngo Xuan Bach, Tu Minh Phuong, Viet-Vu Vu |
المصدر: |
KSII Transactions on Internet & Information Systems; Sep2023, Vol. 17 Issue 9, p2505-2528, 24p |
مصطلحات موضوعية: |
FACIAL expression, DEEP learning, CONVOLUTIONAL neural networks, ATTENTION, HUMAN-computer interaction |
مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of KSII Transactions on Internet & Information Systems is the property of Korean Society for Internet Information and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
قاعدة البيانات: |
Supplemental Index |