• Chỉ mục bởi
  • Năm xuất bản

A Facial Expression Recognition Model using Lightweight Dense-Connectivity Neural Networks for Monitoring Online Learning Activities

Long Hanoi Open University, Hanoi, 100000, Viet Nam|
Tran Tien (57992379200) | Truong Tien (57210971239); Dung | Duong Thang (57220119224); Tung |

International Journal of Modern Education and Computer Science Số 6, năm 2022 (Tập 14, trang 53-64)

ISSN: 20750161

ISSN: 20750161

DOI: 10.5815/ijmecs.2022.06.05

Tài liệu thuộc danh mục:



Tóm tắt tiếng anh
State-of-the-art architectures of convolutional neural networks (CNN) are widely used by authors for facial expression recognition (FER). There are many variants of these models with positive results in studies for FER and successful applications, some well-known models are VGG, ResNet, Xception, EfficientNet, DenseNet. However, these models have considerable complexity for some real-world applications with limitations of computational resources. This paper proposes a lightweight CNN model based on a modern architecture of dense-connectivity with moderate complexity but still ensures quality and efficiency for facial expression recognition. Then, it is designed to be integrated into learning management systems (LMS) for recording and evaluation of online learning activities. The proposed model is to run experiments on some popular datasets for testing and evaluation, the results show that the model is effective and can be used in practice. � 2022, Modern Education and Computer Science Press. All rights reserved.

Xem chi tiết