An Innovative Approach to Face Emotion Recognition Using ‎Antlion Optimization Model

  • Authors

    • C. Shakila Research Scholar, Department of Computing Science and Technology, Vels Institute of Science Technology And Advanced Studies (VISTAS) ‎‎– Pallavaram. Chennai,600117, TN, India
    • T. Kamalakannan Professor, Department of Information Technology, School of Computing Science, Vels Institute of Science Technology and Advanced Studies ‎‎(VISTAS) – Pallavaram, Chennai,600117, TN, India
    https://doi.org/10.14419/xwcnx826

    Received date: July 15, 2025

    Accepted date: July 24, 2025

    Published date: November 1, 2025

  • Emotion Recognition; HALEC; Hybrid Feature Extraction; MSER; Voila-Jones Cascade Object Detectors

  • Abstract

    Emotion analysis is an interesting area of research that contains various integrations of contextual data collected from real-life incidents. ‎Various existing implementations of emotion detection address facial expressions that signify changing moods. The proposed system is ‎suggested by considering the inconsistencies and rankings of various critical parameter concentrations in existing frameworks for emotion ‎detection, such as empathic concern (intention towards others) and fortune and personal distress, which relate to the situation of comfort in ‎response to others' emotions. Emotions need to be understood deeply to recognize the feelings of humans accurately. In the presented ‎system, the feature extraction used for facial expression handling is implemented using a neural network with respect to facial emotions, ‎such as happy, Fear, sad, angry, surprised, and neutral. In practical humans, they can generate various facial expressions during ‎communication that vary in very intense manners. The proposed system-wise hybrid feature extraction and facial expression identification ‎technique, utilizing Viola-Jones cascade object detectors and MSER feature extraction technique with speed-up robust feature extraction ‎‎(SURF) technique, is represented as multimodality network features (MMNF) for classification. The identified features are further classified ‎using an LSTM (Long Short-Term Memory) model, combined with a CNN (Convolutional Neural Network) hybrid architecture. Further, ‎the system parameters are optimized through the Hybrid Ant-Lion optimization technique. The presented system achieved 98% accuracy ‎compared with various state-of-the-art approaches.

  • References

    1. Chu, Hui-Chuan, William Wei-Jen Tsai, Min-Ju Liao, and YuhMin Chen. ”Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning.” Soft Computing: 1-27, 2017.
    2. K. Prabhu, S. SathishKumar, M. Sivachitra, S. Dineshkumar, and P. Sathiyabama, “Facial expression recognition using enhanced convolution neural network with attention mechanism,” Computer Systems Science and Engineering, vol. 41, no. 1, pp. 415–426, 2022. https://doi.org/10.32604/csse.2022.019749.
    3. S. Li and W. Deng, “Deep facial expression recognition: a survey,” IEEE Transactions on Affective Computing, 2020.
    4. P. Foggia, A. Greco, A. Saggese, and M. Vento, "Multitask learning on the edge for effective gender, age, ethnicity, and emotion recognition," En-gineering Applications of Artificial Intelligence, vol. 118, Article ID 105651, 2023. https://doi.org/10.1016/j.engappai.2022.105651.
    5. P. Sharma, "Student engagement detection using emotion analysis, eye tracking, and head movement with machine learning," in Proceedings of the Technology and Innovation in Learning, Teaching and Education: Third International Conference, TECH-EDU 2022, Lisbon, Portugal, September 2022. View at: Google Scholar. https://doi.org/10.1007/978-3-031-22918-3_5.
    6. M. Murugappan and A. Mutawa, “Facial geometric feature extraction based emotional expression classification using machine learning algorithms,” PLoS One, vol. 16, no. 2, Article ID e0247131, 2021.View at: Publisher Site | Google Scholar. https://doi.org/10.1371/journal.pone.0247131.
    7. E. R. Kimonis, B. Le, G. E. Fleming et al., “Facial reactions to emotional films in young children with conduct problems and varying levels of cal-lous unemotional traits,” Journal of Child Psychology and Psychiatry, vol. 64, no. 3, pp. 357–366, 2023.View at: Publisher Site | Google Scholar. https://doi.org/10.1111/jcpp.13701.
    8. M. Sajjad, S. Zahir, A. Ullah, Z. Akhtar, and K. Muhammad, "Human behavior understanding in big multimedia data using CNN-based facial ex-pression recognition," Mobile Networks and Applications, vol. 25, no. 4, pp. 1611–1621, 2019. https://doi.org/10.1007/s11036-019-01366-9.
    9. H. Joseph and B. K. Rajan, "Real-time drowsiness detection using Viola-Jones & KLT," in Proceedings of the 2020 International Conference on Smart Electronics and Communication (ICOSEC), IEEE, Trichy, India, September 2020. https://doi.org/10.1109/ICOSEC49089.2020.9215255.
    10. T. H. Obaida, "Real-time face detection in digital video based on Viola-Jones supported by convolutional neural networks," International Journal of Electrical and Computer Engineering, vol. 12, no. 3, pp. 2088–8708, 2022. https://doi.org/10.11591/ijece.v12i3.pp3083-3091.
    11. Oulefki, Adel, Mustapha Aouache, and Messaoud Bengherabi. "Low-Light Face Image Enhancement Based on Dynamic Face Part Selection." Ibe-rian Conference on Pattern Recognition and Image Analysis, Springer, Cham, 2019. https://doi.org/10.1007/978-3-030-31321-0_8.
    12. Bendjillali, Ridha Ilyas, et al. "Illumination-robust face recognition based on deep convolutional neural networks architectures." Indonesian Journal of Electrical Engineering and Computer Science 18.2 (2020): 1015-1027. https://doi.org/10.11591/ijeecs.v18.i2.pp1015-1027.
    13. Z. Dong, X. Li, B. Lin, and F. Xie, "Multi-Net Fusion: Exploring a Brain-Inspired Neural Network Model for Facial Expression Recognition," in IEEE Access, vol. 11, pp. 93804-93814, 2023, https://doi.org/10.1109/ACCESS.2023.3310398.
    14. M. D’incà, C. Beyan, R. Niewiadomski, S. Barattin, and N. Sebe, "Unleashing the Transferability Power of Unsupervised Pre-Training for Emotion Recognition in Masked and Unmasked Facial Images," in IEEE Access, vol. 11, pp. 90876-90890, 2023, https://doi.org/10.1109/ACCESS.2023.3308047.
    15. R. Khoeun, W. Yookwan, P. Chophuk, A. Rodtook, and K. Chinnasarn, "Emotion Recognition of Partial Face Using Star-Like Particle Polygon Estimation," in IEEE Access, vol. 11, pp. 87558-87570, 2023, https://doi.org/10.1109/ACCESS.2023.3305514.
    16. Y. Ma et al., "What Can Facial Movements Reveal? Depression Recognition and Analysis Based on Optical Flow Using Bayesian Networks," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 3459-3468, 2023, https://doi.org/10.1109/TNSRE.2023.3305351.
    17. Kazemi, V.; Sullivan, J. One millisecond face alignment with an ensemble of regression trees. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 23–28 June 2014; 2014; pp. 1867–1874. https://doi.org/10.1109/CVPR.2014.241.
    18. Zhang, K.; Zhang, Z.; Li, Z.; Qiao, Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 2016, 23, 1499–1503. https://doi.org/10.1109/LSP.2016.2603342.
    19. F. M. Javed Mehedi Shamrat, Z. Tasnim, P. Ghosh, A. Majumder, and M. Z. Hasan, "Personalization of Job Circular Announcement to Appli-cants Using Decision Tree Classification Algorithm," 2020 IEEE International Conference for Innovation in Technology (INOCON), Bangluru, India, 2020, pp. 1-5 https://doi.org/10.1109/INOCON50539.2020.9298253.
    20. Raúl Cid Carro, Juan-Manuel AhuactzinLarios, Edmundo Bonilla Huerta, Roberto Morales Caporal and Federico Ramírez Cruz "Face Recogni-tion Using SURF" ICIC 2015, Part I, LNCS 9225, pp. 316–326, 2015, https://doi.org/10.1007/978-3-319-22180-9_31.
    21. Minaee S, Minaei M, Abdolrashidi A. Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network. Sensors 2019;21. https://doi.org/10.3390/s21093046.
    22. Rio Febriana *, Benedic Matthew Halima, Maria Christinaa, Dimas Ramdhana, Andry,” Facial expression recognition using bidirectional LSTM – CNN”, 1877-0509 © 2023 The Authors. Published by Elsevier B.V.
    23. Ogbonnaya, E., &Wai, Y. M. (2024). Design and Optimization of Energy Harvesting Circuits for Ultra-Low-Power Wearable Electronics. Electron-ics, Communications, and Computing Summit, 2(1), 123–130.
    24. Vishnupriya, T. (2025). Grid-Connected Inverter Topologies for High-Penetration Solar PV Systems: A Comparative Study. Transactions on Power Electronics and Renewable Energy Systems, 44-50.
    25. Alwetaishi, N., &Alzaed, A. (2025). Smart construction materials for sustainable and resilient infrastructure innovations. Innovative Reviews in Engineering and Science, 3(2), 60–72.
    26. Kavitha, M. (2024). Enhancing Security and Privacy in Reconfigurable Computing: Challenges and Methods. SCCTS Transactions on Reconfigu-rable Computing, 1(1), 16-20.
    27. Carvalho, F. M., & Perscheid, T. (2025). Fault-tolerant embedded systems: Reliable operation in harsh environments approaches. SCCTS Journal of Embedded Systems Design and Applications, 2(2), 1–8.
    28. Usikalu, M. R., Okafor, E. N. C., Alabi, D., & Ezeh, G. N. (2023). Data Distinguisher Module Implementation Using CMOS Techniques. Journal of VLSI Circuits and Systems, 5(1), 49–54. https://doi.org/10.31838/jvcs/05.01.07.
    29. Reginald, P. J. (2025). Wavelet-based denoising and classification of ECG signals using hybrid LSTM-CNN models. National Journal of Signal and Image Processing, 1(1), 9–17.
    30. Madhanraj. (2025). Design and Simulation of RF Sensors for Biomedical Implant Communication. National Journal of RF Circuits and Wireless Systems, 2(1), 44–51.
    31. Sindhu, S. (2025). Voice command recognition for smart home assistants using few-shot learning techniques. National Journal of Speech and Audio Processing, 1(1), 22–29.
    32. Velliangiri, A. (2025). Low-Power IoT Node Design for Remote Sensor Networks Utilizing Deep Sleep Protocols. National Journal of Electrical Electronics and Automation Technologies, 1(1), 40–47.

  • Downloads

  • How to Cite

    Shakila, C., & Kamalakannan , T. . (2025). An Innovative Approach to Face Emotion Recognition Using ‎Antlion Optimization Model. International Journal of Basic and Applied Sciences, 14(SI-1), 615-623. https://doi.org/10.14419/xwcnx826