نهج دمج الشبكات العصبية التلافيفية المتعددة لتحسين التعرف على تعبيرات الوجه

Ahmed, A., Yousif, H., Kays, R. and He, Z., 2019. Semantic region of interest and species classification in the deep neural network feature domain. Ecological Informatics, 52, pp. 57–68. https://doi.org/10.1016/j.ecoinf.2019.05.006

Alkababji, A. M. and Abd, S. R., 2021. Half-face-based recognition using principal component analysis. Indonesian Journal of Electrical Engineering and Computer Science, 22, pp. 1404–1410. https://doi.org/10.11591/ijeecs.v22.i3.pp1404-1410

Aly, M., 2025. Revolutionizing online education: Advanced facial expression recognition for real-time student progress tracking via deep learning model. Multimedia Tools and Applications, 84, pp. 12575–12614. https://doi.org/10.1007/s11042-024-19392-5

Avanija, J., Madhavi, K. R., Sunitha, G., Sangapu, S. C., and Raju, S., 2022. Facial expression recognition using convolutional neural network. In Proceedings of 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). pp. 1–7. IEEE. https://doi.org/10.1109/icaitpr51569.2022.9844221

Cao, J., Hu, C., Kong, L. and Yu, Z., 2023. Expression recognition based on a multi-level multi-model fusion deep convolutional neural network. Highlights in Science, Engineering and Technology, 34, pp. 232–237. https://doi.org/10.54097/hset.v34i.5477

Cavallo, F., Semeraro, F., Fiorini, L., Magyar, G., Sinčák, P. and Dario, P., 2018. Emotion modelling for social robotics applications: a review. Journal of Bionic Engineering, 15, pp. 185–203. https://doi.org/10.1007/s42235-018-0015-y

Chouhayebi, H., Mahraz, M. A., Riffi, J. and Tairi, H., 2024. A dynamic fusion of features from deep learning and the HOG-TOP algorithm for facial expression recognition. Multimedia Tools and Applications, 83, pp. 32993–33017. https://doi.org/10.1007/s11042-023-16779-8

Dhall, A., Asthana, A., Goecke, R. and Gedeon, T., 2011. Emotion recognition using PHOG and LPQ features. In Proceedings of 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG). pp. 878–883. IEEE. https://doi.org/10.1109/fg.2011.5771366

Díaz, A. A. O., Tamayo, S. C., De Oliveira, D. N. and Abensur, G. A., 2024. Models for real-time emotion classification: FER-2013 dataset. In Proceedings of Intelligent Systems Conference. Springer Nature Switzerland. pp. 289–304. https://doi.org/10.1007/978-3-031-66431-1_19

Ge, H., Zhu, Z., Dai, Y., Wang, B. and Wu, X., 2022. Facial expression recognition based on deep learning. Computer Methods and Programs in Biomedicine, 215, P. 106621. https://doi.org/10.1016/j.cmpb.2022.106621

He, K., Zhang, X., Ren, S. and Sun, J., 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp. 770–778. https://doi.org/10.1109/cvpr.2016.90

Ji, Y., Hu, Y., Yang, Y., Shen, F. and Shen, H. T., 2019. Cross-domain facial expression recognition via an intra-category common feature and inter-category distinction feature fusion network. Neurocomputing, 333, pp. 231–239. https://doi.org/10.1016/j.neucom.2018.12.037

Krizhevsky, A., Sutskever, I. and Hinton, G. E., 2017. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, pp. 84–90. https://doi.org/10.1145/3065386

Lan, X., Xue, J., Qi, J., Jiang, D., Lu, K. and Chua, T.-S., 2025. Expllm: Towards chain of thought for facial expression recognition. IEEE Transactions on Multimedia, 27, pp. 3069-3081. https://doi.org/10.1109/tmm.2025.3557704

Liang, D., Liang, H., Yu, Z. and Zhang, Y., 2020. Deep convolutional BiLSTM fusion network for facial expression recognition. The Visual Computer, 36, pp. 499–508. https://doi.org/10.1007/s00371-019-01636-3

Liu, W., Song, C., Wang, Y. and Jia, L., 2012. Facial expression recognition based on gabor features and sparse representation. In Proceedings of 2012 12th International Conference on Control Automation Robotics & Vision (ICARCV). pp. 1402–1406. IEEE. https://doi.org/10.1109/ICARCV.2012.6485394

Mollahosseini, A., Chan, D. and Mahoor, M. H., 2016. Going deeper in facial expression recognition using deep neural networks. In Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1–10. IEEE. https://doi.org/10.1109/WACV.2016.7477450

Ni, R., Yang, B., Zhou, X., Cangelosi, A. and Liu, X., 2022. Facial expression recognition through cross-modality attention fusion. IEEE Transactions on Cognitive and Developmental Systems, 15, pp. 175–185. https://doi.org/10.1109/tcds.2022.3150019

Niu, B., Gao, Z. and Guo, B., 2021. Facial expression recognition with LBP and ORB features. Computational Intelligence and Neuroscience, 2021, P. 8828245. https://doi.org/10.1155/2021/8828245

Pierre, C. and Aaron, C., FER-2013 Dataset. Available at: https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data (Accessed: 17 May 2026).

Revina, I. M. and Emmanuel, W. S., 2021. A survey on human face expression recognition techniques. Journal of King Saud University-Computer and Information Sciences, 33, pp. 619–628. https://doi.org/10.1016/j.jksuci.2018.09.002

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A. and Bernstein, M., 2015. Image net large scale visual recognition challenge. International Journal of Computer Vision, 115, pp. 211–252. https://doi.org/10.1007/s11263-015-0816-y

Salloum, S. A., Alomari, K. M., Alfaisal, A. M., Aljanada, R. A. and Basiouni, A., 2025. Emotion recognition for enhanced learning: using AI to detect students’ emotions and adjust teaching methods. Smart Learning Environments, 12, P. 21. https://doi.org/10.1186/s40561-025-00374-5

Shan, C., Gong, S. and Mcowan, P. W., 2009. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27, pp. 803–816. https://doi.org/10.1016/j.imavis.2008.08.005

Shao, J. and Qian, Y., 2019. Three convolutional neural network models for facial expression recognition in the wild. Neurocomputing, 355, pp. 82–92. https://doi.org/10.1016/j.neucom.2019.05.005

Sharma, P., Esengönül, M., Khanal, S. R., Khanal, T. T., Filipe, V. and Reis, M. J., 2018. Student concentration evaluation index in an e-learning context using facial emotion analysis. In Proceedings of International Conference on Technology and Innovation in Learning, Teaching and Education. pp. 529–538. Springer. https://doi.org/10.1007/978-3-030-20954-4_40

Sun, N., Li, Q., Huan, R., Liu, J. and Han, G., 2019. Deep spatial-temporal feature fusion for facial expression recognition in static images. Pattern Recognition Letters, 119, pp. 49–61. https://doi.org/10.1016/j.patrec.2017.10.022

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A., 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp. 1–9. https://doi.org/10.1109/CVPR.2015.7298594

Tan, M. and Le, Q., 2019. EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of International Conference on Machine Learning (ICML). pp. 6105–6114. PMLR. https://doi.org/10.48550/arXiv.1905.11946

Tang, X., Gong, Y., Xiao, Y., Xiong, J. and Bao, L., 2025. Facial expression recognition for probing students’ emotional engagement in science learning. Journal of Science Education and Technology, 34, pp. 13–30. https://doi.org/10.1007/s10956-024-10143-7

Tang, Y., Zhang, X. M. and Wang, H., 2018. Geometric-convolutional feature fusion based on learning propagation for facial expression recognition. IEEE Access, 6, pp. 42532–42540. https://doi.org/10.1109/access.2018.2858278

Wang, W., Xu, K., Niu, H. and Miao, X., 2020. Emotion recognition of students based on facial expressions in online education based on the perspective of computer simulation. Complexity, 2020, P. 4065207. https://doi.org/10.1155/2020/4065207

Xie, S. and Hu, H., 2018. Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Transactions on Multimedia, 21, pp. 211–220. https://doi.org/10.1109/TMM.2018.2844085

Xu, L., Yang, A., Fei, M. and Zhou, W., 2018. Brief technical analysis of facial expression recognition. In Proceedings of International Conference on Intelligent Computing for Sustainable Energy and Environment. pp. 302–310. Springer. https://doi.org/10.1007/978-981-13-2384-3_28

Yu, M., Zheng, H., Peng, Z., Dong, J. and Du, H., 2020. Facial expression recognition based on a multi-task global-local network. Pattern Recognition Letters, 131, pp. 166–171. https://doi.org/10.1016/j.patrec.2020.01.016

Zahara, L., Musa, P., Wibowo, E. P., Karim, I. and Musa, S. B., 2020. The facial emotion recognition (FER-2013) dataset for prediction system of micro-expressions face using the convolutional neural network (CNN) algorithm based Raspberry Pi. In Proceedings of 2020 Fifth International Conference on Informatics and Computing (ICIC). pp. 1–9. IEEE. https://doi.org/10.1109/ICIC50835.2020.9288560

Zhu, M. and Wei, L., 2024. A multi-channel convolutional neural network based on attention mechanism fusion for facial expression recognition. Applied Mathematics and Nonlinear Sciences, 9, P. 2. https://doi.org/10.2478/amns.2023.1.00084