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

Full Text (PDF, 951KB), PP.53-64

Views: 0 Downloads: 0


Duong Thang Long 1,* Truong Tien Tung 1 Tran Tien Dung 1

1. Hanoi Open University, Hanoi, 100000, Viet Nam

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2022.06.05

Received: 17 Sep. 2022 / Revised: 16 Oct. 2022 / Accepted: 20 Nov. 2022 / Published: 8 Dec. 2022

Index Terms

Deep learning, Convolution neural network, Dense-Connectivity networks, Facial expression recognition


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.

Cite This Paper

Duong Thang Long, Truong Tien Tung, Tran Tien Dung, "A Facial Expression Recognition Model using Lightweight Dense-Connectivity Neural Networks for Monitoring Online Learning Activities", International Journal of Modern Education and Computer Science(IJMECS), Vol.14, No.6, pp. 53-64, 2022. DOI:10.5815/ijmecs.2022.06.05


[1]D.T.Long, "A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning," I.J. Modern Education and Computer Science, vol. 6, pp. 16-28, 2020.
[2]D.T.Long, "A Facial Expressions Recognition Method Using Residual Network Architecture for Online Learning Evaluation," Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 25, no. 6, pp. 1-10, 2021.
[3]L.Yong, Z.Jiabei, S.Shiguang and C.Xilin, "Occlusion Aware Facial Expression Recognition Using CNN With Attention Mechanism," IEEE Transactions on Image Processing, vol. 28, no. 5, pp. 2439-2450, 2019.
[4]L.Shan and W.Deng, "Deep Facial Expression Recognition: A Survey," IEEE Transactions on Affective Computing, Vols. 1949-3045, pp. 1-20, 2020.
[5]E.Derman and A.A.Salah, "Continuous Real-Time Vehicle Driver Authentication Using Convolutional Neural Network Based Face Recognition," 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), 2018.
[6]S. Li and W. Deng, "Deep Facial Expression Recognition: A Survey," IEEE Transactions on Affective Computing, vol. 13, no. 3, pp. 1195-1215, 2022.
[7]L.Patrick, C.F.Jeffrey, K.Takeo, S.Jason and A.Zara, "The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression," IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, Vols. eISSN: 2160-7516, pp. 1-8, 2010.
[8]M.Longbiao, Y.Yan, X.Jing-Hao and W.Hanzi, "Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification," IEEE Transactions on Affective Computing, no. DOI 10.1109/TAFFC.2020.2969189, pp. 1-11, 2020.
[9]M.A.Shakik, D.Issam and D.Elio, "Facial expression recognition using three-stage support vector machines," Visual Computing for Industry, Biomedicine, and Art, vol. 2, no. 24, pp. https://doi.org/10.1186/s42492-019-0034-5, 2019.
[10]M.Wang and W.Deng, "Deep Face Recognition: A Survey," Neurocomputing, vol. 429, pp. 215-244, 2021.
[11]S.-C. Lai, C.-Y. Chen and J.-H. Li, "Efficient Recognition of Facial Expression with Lightweight Octave Convolutional Neural Network," Journal of Imaging Science and Technology, pp. 040402.1-9, 2022.
[12]A. Greco, N. Strisciuglio, M. Vento and V. Vigilante, "Benchmarking deep networks for facial emotion recognition in the wild," Multimedia Tools and Applications, pp. https://doi.org/10.1007/s11042-022-12790-7, 2022.
[13]M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," Proceedings of the 36th International Conference on Machine Learning, pp. 6105-6114, 2019.
[14]M.Z.Alom, T.M.Taha and C.Yakopcic, "A State-of-the-Art Survey on Deep Learning Theory and Architectures," Electronics, vol. 8, no. 292, pp. 1-67, 2019.
[15]M.Sandler, A.Howard, M.Zhu, A.Zhmoginov and L.C.Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4510-4520, 2018.
[16]G. Huang, Z. Liu, L. V. D. Maaten and K. Q. Weinberger, "Densely Connected Convolutional Networks," IEEE Conference on Computer Vision and Pattern Recognition (CVPR), no. ISSN:1063-6919, pp. 1-9, 2018.
[17]G. Zhao, H. Yang and M. Yu, "Expression Recognition Method Based on a Lightweight Convolutional Neural Network," IEEE Access, vol. 18, pp. 38528 - 38537, 2020.
[18]R. R. Devaram and A. Cesta, "LEMON: A Lightweight Facial Emotion Recognition System for Assistive Robotics Based on Dilated Residual Convolutional Neural Networks," Sensors, vol. 22, no. 3366, pp. 1-20, 2022.
[19]N. Zhou, R. Liang and W. Shi, "A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection," IEEE Access, vol. 9, pp. 5573 - 5584, 2020.
[20]P. N. R. Bodavarapu and P. Srinivas, "An Optimized Neural Network Model for Facial Expression Recognition over Traditional Deep Neural Networks," International Journal of Advanced Computer Science and Applications, vol. 12, no. 7, pp. 443-451, 2021.
[21]Y. Nan, J. Ju, Q. Hua, H. Zhang and B. Wang, "A-MobileNet: An approach of facial expression recognition," Alexandria Engineering Journal, vol. 61, p. 4435–4444, 2022.
[22]P.Simone, F.Alessandro and A.Luigi, "Evaluation of Data Augmentation Techniques for Facial Expression Recognition Systems," Electronics, vol. 9, no. 1892, doi:10.3390/electronics9111892, pp. 1-12, 2020.
[23]D.P.Kingma and J.L.Ba, "Adam: A Method For Stochastic Optimization," CoRR, no. https://arxiv.org/abs/1412.6980, 2015.
[24]Y. Huang, F. Chen, S. Lv and X. Wang, "Facial Expression Recognition: A Survey," Symmetry, vol. 11, no. 1189 (doi:10.3390/sym11101189), pp. 1-28, 2019.
[25]W.Deng and S. Li, "Deep Facial Expression Recognition: A Survey," IEEE Transactions on Affective Computing, vol. 13, pp. 1195-1215, 2022.
[26]S. Minaee, M. Minaei and A. Abdolrashidi, "Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network," Sensors, vol. 21, no. 3046 (https://doi.org/10.3390/s21093046), pp. 1-16, 2021.
[27]R. Zhao, T. Liu, J. Xiao, D. P. Lun and K.-M. Lam, "Deep Multi-task Learning for Facial Expression Recognition and Synthesis Based on Selective Feature Sharing," 25th International Conference on Pattern Recognition (ICPR), pp. 4412-4419, 2020.