Otwarty dostęp

Efficient DenseNet Model with Fusion of Channel and Spatial Attention for Facial Expression Recognition

  
23 mar 2024

Zacytuj
Pobierz okładkę

Long, D. T., T. T. Tung, T. T. 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, Vol. 6, 2022, pp. 53-64.Search in Google Scholar

Long, D. T. A Facial Expressions Recognition Method Using Residual Network Architecture for Online Learning Evaluation. – Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol. 25, 2021, No 6, pp. 953-962. DOI: https://doi.org/10.20965/jaciii.2021.p0953.Search in Google Scholar

Wu, X., J. He, Q. Huang, C. Huang, J. Zhu, X. Huang, H. Fujita. FER-CHC: Facial Expression Recognition with Cross-Hierarchy Contrast. – Applied Soft Computing, Vol. 145, 2023, pp. 1-12. DOI: https://doi.org/10.1016/j.asoc.2023.110530.Search in Google Scholar

Huang, G., Z. Liu, L. V. D. Maaten, K. Q. Weinberger. Densely Connected Convolutional Networks. – In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017, pp. 2261-2269. DOI: https://doi.org/10.1109/CVPR.2017.243.Search in Google Scholar

Guo, M., T. Xu, J. Liu, Z. Liu, P. Jiang, T. Mu, S. Zhang, R. Martin, M. Cheng, S. Hu. Attention Mechanisms in Computer Vision: A Survey. – Computational Visual Media, Vol. 8, 2022, No 3, pp. 331-368. DOI: https://doi.org/10.1007/s41095-022-0271-y.Search in Google Scholar

Alom, M., T. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. Nasrin, M. Hasan, B. Essen, A. Awwal, V. Asari. A State-of-the-Art Survey on Deep Learning Theory and Architectures. – Electronics, Vol. 8, 2019, No 292, pp. 1-67.Search in Google Scholar

Deng, W., S. Li. Deep Facial Expression Recognition: A Survey. – IEEE Transactions on Affective Computing, Vol. 13, 2022, pp. 1195-1215.Search in Google Scholar

Nan, Y., J. Ju, Q. Hua, H. Zhang, B. Wang. A-MobileNet: An Approach of Facial Expression Recognition. – Alexandria Engineering Journal, Vol. 61, 2022, pp. 4435-4444. DOI: https://doi.org/10.1016/j.aej.2021.09.066.Search in Google Scholar

Bhatti, Y., A. Jamil, N. Nida, M. Yousaf, S. Viriri, S. Velastin. Facial Expression Recognition of Instructor Using Deep Features and Extreme Learning Machine. – In: Computational Intelligence and Neuroscience. Vol. 2021. 2021, pp. 1-17. DOI: https://doi.org/10.1155/2021/5570870.Search in Google Scholar

Cao, Y. An Expression Recognition Model Based on Channel and Spatial Attention Fusion. – Journal of Physics: Conference Series, 2022, pp. 1-6. DOI: 10.1088/1742-6596/2363/1/012016.Search in Google Scholar

Devaram, R. R., A. Cesta. LEMON: A Lightweight Facial Emotion Recognition System for Assistive Robotics Based on Dilated Residual Convolutional Neural Networks. – Sensors, Vol. 22, 2022, No 3366, pp. 1-20.Search in Google Scholar

Gan, C., J. Xiao, Z. Wang, Z. Zhang, Q. Zhu. Facial Expression Recognition Using Densely Connected Convolutional Neural Network and Hierarchical Spatial Attention. – Image and Vision Computing, Vol. 117, 2022, No 104342, pp. 1-9. DOI: https://doi.org/10.1016/j.imavis.2021.104342.Search in Google Scholar

Lai, S., C. Chen, J. Li. Efficient Recognition of Facial Expression with Lightweight Octave Convolutional Neural Network. – Journal of Imaging Science and Technology, Vol. 66, 2022, No 4, pp. 040402-1-040402-9.Search in Google Scholar

Zhu, Q., Q. Mao, H. Jia, O. Noi, J. Tu. Convolutional Relation Network for Facial Expression Recognition in the Wild with Few-Shot Learning. – Expert Systems with Applications, Vol. 189, 2022, No 116046, pp. 1-9. DOI: https://doi.org/10.1016/j.eswa.2021.116046.Search in Google Scholar

Chen, X., X. Zheng, K. Sun, W. Liu, Y. Zhang. Self-Supervised Vision Transformer-Based Few-Shot Learning for Facial Expression Recognition. – Information Sciences, Vol. 634, 2023, pp. 206-226. DOI: https://doi.org/10.1016/j.ins.2023.03.105.Search in Google Scholar

Xiao, J., C. Gan, Q. Zhu, Y. Zhu, G. Liu. CFNet: Facial Expression Recognition via Constraint Fusion under Multi-Task Joint Learning Network. – Applied Soft Computing, Vol. 141, 2023, No 110312, pp. 1-12. DOI: https://doi.org/10.1016/j.asoc.2023.110312.Search in Google Scholar

Li, J., K. Jin, D. Zhou, N. Kubota, Z. Ju. Attention Mechanism-Based CNN for Facial Expression Recognition. – Neurocomputing, Vol. 411, 2020, pp. 340-350. DOI: https://doi.org/10.1016/j.neucom.2020.06.014.Search in Google Scholar

Yu, W,. H. Xu. Co-Attentive Multi-Task Convolutional Neural Network for Facial Expression Recognition. – Pattern Recognition, Vol. 123, 2022, No 108401, pp. 1-11. DOI: https://doi.org/10.1016/j.patcog.2021.108401.Search in Google Scholar

Minaee, S., M. Minaei, A. Abdolrashidi. Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. – Sensors, Vol. 21, No 3046, 2021. DOI: https://doi.org/10.3390/s21093046.Search in Google Scholar

Lyons, M., S. Akamatsu, M. Kamachi, J. Gyoba. Coding Facial Expressions with Gabor Wavelets. – In: Proc of 3rd IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200-205. DOI: https://doi.org/10.48550/arXiv.2009.05938.Search in Google Scholar

Lucey, P., J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar. The Extended Cohn-Kanade Dataset (CK+): A Complete Dataset for Action Unit and Emotion-Specified Expression. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition – Workshops, 2010, pp. 94-101. DOI: https://doi.org/10.1109/CVPRW.2010.5543262.Search in Google Scholar

Zhao, G., X. Huang, M. Taini, S. Z. Li, M. Pietikäinen. Facial Expression Recognition from Near-Infrared Videos. – Image and Vision Computing, Vol. 29, 2011, pp. 607-619.Search in Google Scholar

Ellen, G., D. R. Rudi, L. Lemke, V. Bruno. The Karolinska Directed Emotional Faces: A Validation Study. – Cognition & Emotion, Vol. 22, 2008, No 6, pp. 1094-1118.Search in Google Scholar

Li, S., W. Deng, J. Du. Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild. – In: Proc of IEEE Conference on Computer Vision and Pattern Recognition (CVPR’17), 2017, pp. 2584-2593.Search in Google Scholar

Long, D. T. Efficient Multi-Task CNN for Face and Facial Expression Recognition Using Residual and Dense Architectures for Application in Monitoring Online Learning. – International Journal of Fuzzy Logic and Intelligent Systems, Vol. 23, 2023, No 3, pp. 229-243. DOI: http://doi.org/10.5391/IJFIS.2023.23.3.229.Search in Google Scholar

Zhou, N., R. Liang, W. Shi. A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection. – IEEE Access, Vol. 9, 2021, pp. 5573-5584. DOI: 10.1109/ACCESS.2020.3046715.Search in Google Scholar

Kollias, D., V. Sharmanska, S. Zafeiriou. Distribution Matching for Heterogeneous Multi-Task Learning: A Large-Scale Face Study. – IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. DOI: https://doi.org/10.48550/arXiv.2105.03790.Search in Google Scholar

Farzaneh, A. H., X. Qi. Facial Expression Recognition in the Wild via Deep Attentive Center Loss. – In: Proc of IEEE Winter Conference on Applications of Computer Vision (WACV’21), 2021, pp. 2401-2410. DOI: 10.1109/WACV48630.2021.00.Search in Google Scholar

Ming, Z., J. Xia, M. Luqman, J.-C. Burie, K. Zhao. Dynamic Multi-Task Learning for Face Recognition with Facial Expression. – In: Proc. of Lightweight Face Recognition Challenge Workshop during the 2019 International Conference on Computer Vision (ICCV’19), 2019. DOI: https://doi.org/10.48550/arXiv.1911.03281.Search in Google Scholar

Język:
Angielski
Częstotliwość wydawania:
4 razy w roku
Dziedziny czasopisma:
Informatyka, Technologia informacyjna