Proceedings of International Conference on Applied Innovation in IT
2025/06/27, Volume 13, Issue 2, pp.69-77

A Hybrid Deep Learning Model for Facial Emotion Recognition: Combining Multi-Scale Features, Dynamic Attention, and Residual Connections


Muthana Salih Mahdi, Zaydon Latif Ali, Ahmed Ramzi Rashid, Noor Khalid Ibrahim and Abdulghafor Waedallah Abdulghafour


Abstract: Facial emotion recognition is still a challenging task in computer vision because human facial expressions are very subtle and complex. In this paper, we address this issue and propose a novel deep-learning framework that combines multi-scale feature extraction with a dynamic attention mechanism and improved residual connection. The research aims to create a reliable system that identifies facial expressions correctly in different circumstances. The proposed method was validated rigorously on a standard face expression recognition data set, with an impressive overall accuracy of 96.1%. Additionally, the model performed remarkably well on extra metrics like precision, recall, and F1-score. These findings highlight the model’s ability to learn and distinguish subtle features in human faces, leading to improved performance compared to conventional methods. In summary, this research makes a noteworthy contribution to affective computing by paving the way for the future development of real-time systems that can recognize human emotions, enabling numerous potential applications in the fields of mental health assessment, human-computer interaction, and adaptive user interfaces.

Keywords: Emotion Classification, Convolutional Neural Networks, Facial Emotion Recognition, Multiscale Features, Deep Learning, Attention Mechanism.

DOI: 10.25673/120395

Download: PDF

References:

  1. M. K. Chowdary, T. N. Nguyen, and D. J. Hemanth, "Deep learning-based facial emotion recognition for human–computer interaction applications," Neural Comput. Appl., vol. 35, no. 32, pp. 23311-23328, Nov. 2023, [Online]. Available: https://doi.org/10.1007/s00521-021-06012-8.
  2. P. Yu, X. He, H. Li, H. Dou, Y. Tan, H. Wu, and B. Chen, "FMLAN: A novel framework for cross-subject and cross-session EEG emotion recognition," Biomed. Signal Process. Control, vol. 100, article 106912, 2025, [Online]. Available: https://doi.org/10.1016/j.bspc.2024.106912.
  3. A. L. Cîrneanu, D. Popescu, and D. Iordache, "New trends in emotion recognition using image analysis by neural networks, a systematic review," Sensors, vol. 23, no. 16, p. 7092, Aug. 2023, [Online]. Available: https://doi.org/10.3390/s23167092.
  4. E. Hato, "Extracting descriptive frames from informational videos," Iraqi J. Sci., vol. 64, no. 4, pp. 4260-4277, 2023, [Online]. Available: https://doi.org/10.24996/ijs.2023.64.8.43.
  5. S. Hazmoune and F. Bougamouza, "Using transformers for multimodal emotion recognition: Taxonomies and state of the art review," Eng. Appl. Artif. Intell., vol. 133, article 108339, 2024, [Online]. Available: https://doi.org/10.1016/j.engappai.2024.108339.
  6. Y. M. Mohialden, N. M. Hussien, Q. A. Jabbar, M. A. Mohammed, and T. Sutikno, "An internet of things-based medication validity monitoring system," Indones. J. Electr. Eng. Comput. Sci., vol. 26, no. 2, pp. 932-938, 2022, [Online]. Available: http://doi.org/10.11591/ijeecs.v26.i2.pp932-938.
  7. A. Basim and A. Sadiq, "Enhancement of low light images using residual deep learning," in Proc. Int. Conf. Innov. Intell. Informatics, Netw. Cybersecurity, Cham, Switzerland: Springer Nature, Oct. 2024, pp. 119-132, [Online]. Available: https://doi.org/10.1007/978-3-031-81065-7_8.
  8. M. S. Mahdi and Z. L. Ali, "A lightweight algorithm to protect the web of things in IoT," in Emerging Technology Trends in Internet of Things and Computing, Cham, Switzerland: Springer Int. Publ., Mar. 2022, pp. 46-60, [Online]. Available: https://doi.org/10.1007/978-3-030-97255-4_4.
  9. H. R. Shakir, "Secure selective image encryption based on wavelet domain, 3D‑chaotic map, and discrete fractional random transform," Int. J. Intell. Eng. Syst., vol. 16, no. 6, pp. 85-98, 2023, [Online]. Available: https://doi.org/10.22266/ijies2023.1231.80.
  10. A. R. Rashid, Z. L. Ali, and G. H. A. Alshmeel, "Developing an artificial intelligence-based system to detect fraud and corruption in government," in Proc. 2022 Int. Congr. on Human–Computer Interaction, Optimization and Robotic Applications (HORA), Baghdad, Iraq: IEEE, Jun. 2022, pp. 1-8, [Online]. Available: https://doi.org/10.1109/HORA55278.2022.9799900.
  11. M. Jagadeesh and G. Baranidharan, "Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video," Concurrency Comput. Pract. Exp., vol. 34, no. 5, May 2022, [Online]. Available: https://doi.org/10.1002/cpe.7373.
  12. B. Fu, Y. Mao, S. Fu, Y. Ren, and Z. Luo, "Blindfold attention: Novel mask strategy for facial expression recognition," in Proc. ACM Comput. Graph. Interact. Tech., New York, USA, 2022, pp. 1-10, [Online]. Available: https://doi.org/10.1145/3512527.3531416.
  13. V. X. Chi and P. C. Vinh, "Facial sentiment recognition using artificial intelligence techniques," EAI Endorsed Trans. Context-Aware Syst. Appl., vol. 10, no. 3, pp. 112-120, 2023, [Online]. Available: https://doi.org/10.4108/eetcasa.v9i1.3930.
  14. Y. Chen and M. Zhang, "Research on face emotion recognition algorithm based on deep learning neural network," Appl. Math. Nonlinear Sci., vol. 8, no. 1, pp. 59-67, 2023, [Online]. Available: https://doi.org/10.2478/amns.2023.2.00533.
  15. H. Shahzad, S. Bhatti, A. Jaffar, M. Rashid, and S. Akram, "Multi-modal CNN features fusion for emotion recognition: A modified Xception model," IEEE Access, vol. 11, pp. 12345-12356, 2023, [Online]. Available: https://doi.org/10.1109/ACCESS.2023.3310428.
  16. X. Wang, Y. Wang, and D. Zhang, "Complex emotion recognition via facial expressions with label noises self-cure relation networks," Comput. Intell. Neurosci., vol. 2023, article 987654, 2023, [Online]. Available: https://doi.org/10.1155/2023/7850140.
  17. S. Wang, J. Qu, Y. Zhang, and Y. Zhang, "Multimodal emotion recognition from EEG signals and facial expressions," IEEE Access, vol. 11, pp. 23456-23468, 2023, [Online]. Available: https://doi.org/10.1109/access.2023.3263670.
  18. R. Tshibangu and J. R. Tapamo, "Improving facial emotional recognition using convolution neural network with minimal layers," in Electronics, Communications, and Networks, Amsterdam, The Netherlands: IOS Press, 2024, pp. 672-682, [Online]. Available: https://doi.org/10.3233/FAIA231252.
  19. J. Pan, W. Fang, Z. Zhang, B. Chen, Z. Zhang, and S. Wang, "Multimodal emotion recognition based on facial expressions, speech, and EEG," IEEE Open J. Eng. Med. Biol., vol. 5, pp. 396-403, 2024, [Online]. Available: https://doi.org/10.1109/OJEMB.2023.3240280.
  20. Z. Wu and D. Pan, "The application and optimization of deep learning in recognizing student learning emotions," Trait. Signal, vol. 41, no. 1, pp. 391-399, 2024, [Online]. Available: https://doi.org/10.18280/ts.410133.
  21. Face expression recognition dataset, Kaggle, 2017, [Online]. Available: https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset.
  22. Facial emotion recognition dataset, Kaggle, 2018, [Online]. Available: https://www.kaggle.com/datasets/chiragsoni/ferdata.
  23. Facial Emotion dataset, Kaggle, 2019, [Online]. Available: https://www.kaggle.com/datasets/mh0386/facial-emotion.


    HOME

       - Conference
       - Journal
       - Paper Submission to Journal
       - For Authors
       - For Reviewers
       - Important Dates
       - Conference Committee
       - Editorial Board
       - Reviewers
       - Last Proceedings


    PROCEEDINGS

       - Volume 13, Issue 2 (ICAIIT 2025)
       - Volume 13, Issue 1 (ICAIIT 2025)
       - Volume 12, Issue 2 (ICAIIT 2024)
       - Volume 12, Issue 1 (ICAIIT 2024)
       - Volume 11, Issue 2 (ICAIIT 2023)
       - Volume 11, Issue 1 (ICAIIT 2023)
       - Volume 10, Issue 1 (ICAIIT 2022)
       - Volume 9, Issue 1 (ICAIIT 2021)
       - Volume 8, Issue 1 (ICAIIT 2020)
       - Volume 7, Issue 1 (ICAIIT 2019)
       - Volume 7, Issue 2 (ICAIIT 2019)
       - Volume 6, Issue 1 (ICAIIT 2018)
       - Volume 5, Issue 1 (ICAIIT 2017)
       - Volume 4, Issue 1 (ICAIIT 2016)
       - Volume 3, Issue 1 (ICAIIT 2015)
       - Volume 2, Issue 1 (ICAIIT 2014)
       - Volume 1, Issue 1 (ICAIIT 2013)


    PAST CONFERENCES

       ICAIIT 2025
         - Photos
         - Reports

       ICAIIT 2024
         - Photos
         - Reports

       ICAIIT 2023
         - Photos
         - Reports

       ICAIIT 2021
         - Photos
         - Reports

       ICAIIT 2020
         - Photos
         - Reports

       ICAIIT 2019
         - Photos
         - Reports

       ICAIIT 2018
         - Photos
         - Reports

    ETHICS IN PUBLICATIONS

    ACCOMODATION

    CONTACT US

 

        

         Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0


                                                   This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License


           ISSN 2199-8876
           Publisher: Edition Hochschule Anhalt
           Location: Anhalt University of Applied Sciences
           Email: leiterin.hsb@hs-anhalt.de
           Phone: +49 (0) 3496 67 5611
           Address: Building 01 - Red Building, Top floor, Room 425, Bernburger Str. 55, D-06366 Köthen, Germany

        site traffic counter

Creative Commons License
Except where otherwise noted, all works and proceedings on this site is licensed under Creative Commons Attribution-ShareAlike 4.0 International License.