Facial Image Modeling Using Deep Learning Techniques in the Management of Administrative Processes at UTM
Facial emotion recognition using deep learning techniques applied to administrative processes at the Technical University of Manabí
DOI:
https://doi.org/10.53591/iti.v17i24.2665Keywords:
Deep learning, MobileNetV1, administrative processes, facial emotion recognition, convolutional neural networks.Abstract
Context: Higher education institutions promote automated solutions to optimize processes and services; this study designed a facial emotion recognition prototype applied to administrative processes at Universidad Técnica de Manabí (UTM). Method: A custom convolutional neural network (CNN) optimized for 48×48 grayscale images was compared against a pretrained MobileNetV1, following a reproducible pipeline of preprocessing, data augmentation, and validation across six emotion classes (happiness, anger, fear, neutral, sadness, and disgust). Results: The custom CNN achieved the best performance with an accuracy of 0.88 versus 0.80 for MobileNetV1; the best model was integrated into a prototype tested in enrollment scenarios, where the neutral emotion showed the highest frequency and confidence. Conclusions: In this context, convolutional neural networks are confirmed as suitable for image recognition focused on user service management in public services; future work should strengthen stratified validation, compare against reference datasets, and establish ethical criteria to support generalization and responsible use of sensitive data.
References
Agung, E. S., Rifai, A. P. & Wijayanto, T. (2024). Image-based facial emotion recognition using convolutional neural network on emognition dataset. Scientific Reports, 14(1), 1–22. https://doi.org/10.1038/S41598-024-65276-X;SUBJMETA=117,258,2811,477,631,639,705;KWRD=COMPUTER+SCIENCE,HUMAN+BEHAVIOUR,INFORMATION+TECHNOLOGY
Akhand, M. A. H., Roy, S., Siddique, N., Kamal, M. A. S. & Shimamura, T. (2021). Facial Emotion Recognition Using Transfer Learning in the Deep CNN. Electronics 2021, Vol. 10, Page 1036, 10(9), 1036. https://doi.org/10.3390/ELECTRONICS10091036
Alruwais, N. & Zakariah, M. (2025). Detecting Student Engagement with Convolution Neural Network and Facial Expression Recognition. Traitement Du Signal, 42(2). https://doi.org/10.18280/TS.420229
Appalanaidu, M. V. & Kumaravelan, G. (2023). Rice plant nutrient deficiency classification using modified MOBILENET convolutional neural network. International Journal of Modeling, Simulation, and Scientific Computing, 14(1). https://doi.org/10.1142/S1793962322430036;REQUESTEDJOURNAL:JOURNAL:IJMSSC;PAGEGROUP:STRING:PUBLICATION
Babu, T., Ebin, P. M. & Nair, R. R. (2024). Real-Time Facial Expression Recognition Using Deep Learning for Enhanced Human-Machine Interactions. 2024 IEEE Flagship International BIT Conference: Next Generation Applications in Green Energy Technology, BITCON 2024. https://doi.org/10.1109/BITCON63716.2024.10984634
Dhope, P. & Neelagar, M. B. (2022). Real-Time Emotion Recognition from Facial Expressions using Artificial Intelligence. 2022 2nd International Conference on Artificial Intelligence and Signal Processing, AISP 2022. https://doi.org/10.1109/AISP53593.2022.9760654
Díaz-Ramírez, J. & Díaz-Ramírez, J. (2021). Aprendizaje Automático y Aprendizaje Profundo. Ingeniare. Revista Chilena de Ingeniería, 29(2), 180–181. https://doi.org/10.4067/S0718-33052021000200180
Dwijayanti, S., Iqbal, M. & Suprapto, B. Y. (2022). Real-Time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network. IEEE Access, 10, 89876–89886. https://doi.org/10.1109/ACCESS.2022.3200762
Gursesli, M. C., Lombardi, S., Duradoni, M., Bocchi, L., Guazzini, A. & Lanata, A. (2024). Facial Emotion Recognition (FER) Through Custom Lightweight CNN Model: Performance Evaluation in Public Datasets. IEEE Access, 12, 45543–45559. https://doi.org/10.1109/ACCESS.2024.3380847
Haq, H. B. U., Akram, W., Irshad, M. N., Kosar, A. & Abid, M. (2024). Enhanced Real-Time Facial Expression Recognition Using Deep Learning. Acadlore Transactions on AI and Machine Learning, 3(1), 24–35. https://doi.org/10.56578/ATAIML030103
Hassan, E., Bhatnagar, R., El-Hafeez, T. A. & Shams, M. Y. (2025). Detection of Suicide and Depression for Early Intervention and Initiative-taking Mental Healthcare. Proceedings of IEEE International Conference on Signal Processing,Computing and Control, 99–104. https://doi.org/10.1109/ISPCC66872.2025.11039547
Hassouneh, A., Mutawa, A. M. & Murugappan, M. (2020). Development of a Real-Time Emotion Recognition System Using Facial Expressions and EEG based on machine learning and deep neural network methods. Informatics in Medicine Unlocked, 20, 100372. https://doi.org/10.1016/J.IMU.2020.100372
Hussain, S. A., Reddy, N. Y., Srivardhan, J., Sharma, A., Sharma, S. & Gochhait, S. (2024). Automated Emotion Recognition from Facial Expressions using Convolutional Neural Network. 2024 5th International Conference on Data Analytics for Business and Industry, ICDABI 2024, 139–143. https://doi.org/10.1109/ICDABI63787.2024.10800158
Kartali, A., Roglic, M., Barjaktarovic, M., Duric-Jovicic, M. & Jankovic, M. M. (2018). Real-time Algorithms for Facial Emotion Recognition: A Comparison of Different Approaches. 2018 14th Symposium on Neural Networks and Applications, NEUREL 2018. https://doi.org/10.1109/NEUREL.2018.8587011
Kaur, M. & Kumar, M. (2024). Facial emotion recognition: A comprehensive review. Expert Systems, 41(10), e13670. https://doi.org/10.1111/EXSY.13670;WGROUP:STRING:PUBLICATION
Mohammed, O. N. (2024). Enhancing Pulmonary Disease Classification in Diseases: A Comparative Study of CNN and Optimized MobileNet Architectures. Journal of Robotics and Control (JRC), 5(2), 427–440. https://doi.org/10.18196/JRC.V5I2.21422
Mukul, E. & Büyüközkan, G. (2023). Digital transformation in education: A systematic review of education 4.0. Technological Forecasting and Social Change, 194, 122664. https://doi.org/10.1016/J.TECHFORE.2023.122664
Ocen, S., Elasu, J., Aarakit, S. M. & Olupot, C. (2025a). Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges. Frontiers in Education, 10, 1530247. https://doi.org/10.3389/FEDUC.2025.1530247/BIBTEX
Ocen, S., Elasu, J., Aarakit, S. M. & Olupot, C. (2025b). Artificial intelligence in higher education institutions: review of innovations, opportunities and challenges. Frontiers in Education, 10, 1530247. https://doi.org/10.3389/FEDUC.2025.1530247/BIBTEX
Opitz, J. (2024). A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice. Transactions of the Association for Computational Linguistics, 12, 820–836. https://doi.org/10.1162/tacl_a_00675
Pathar, R., Adivarekar, A., Mishra, A. & Deshmukh, A. (2019). Human Emotion Recognition using Convolutional Neural Network in Real Time. Proceedings of 1st International Conference on Innovations in Information and Communication Technology, ICIICT 2019. https://doi.org/10.1109/ICIICT1.2019.8741491
Pereira, R., Mendes, C., Ribeiro, J., Ribeiro, R., Miragaia, R., Rodrigues, N., Costa, N. & Pereira, A. (2024). Systematic Review of Emotion Detection with Computer Vision and Deep Learning. Sensors, 24(11). https://doi.org/10.3390/S24113484,
Pise, A. A., Alqahtani, M. A., Verma, P., Purushothama, K., Karras, D. A., Prathibha, S. & Halifa, A. (2022). Methods for Facial Expression Recognition with Applications in Challenging Situations. Computational Intelligence and Neuroscience, 2022, 9261438. https://doi.org/10.1155/2022/9261438
Poongodai, A., Bhavitha, V., Hemanth, D., Jahnavi, G. & Huzma, S. (2025). Human Emotion Detection Through Real-Time Facial Expressions Using Deep Learning. Proceedings of the 6th International Conference on Inventive Research in Computing Applications, ICIRCA 2025, 1298–1303. https://doi.org/10.1109/ICIRCA65293.2025.11089747
Rakesh, K. R., Namita, G. R. & Kulkarni, R. (2022). Image Recognition, Classification and Analysis Using Convolutional Neural Networks. 2022 1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022. https://doi.org/10.1109/ICEEICT53079.2022.9768474
Rathour, N., Singh, R., Gehlot, A., Vaseem Akram, S., Kumar Thakur, A. & Kumar, A. (2022). The decadal perspective of facial emotion processing and Recognition: A survey. Displays, 75, 102330. https://doi.org/10.1016/J.DISPLA.2022.102330
Rehman, A., Mujahid, M., Elyassih, A., AlGhofaily, B. & Bahaj, S. A. O. (2025). Comprehensive Review and Analysis on Facial Emotion Recognition: Performance Insights into Deep and Traditional Learning with Current Updates and Challenges. Computers, Materials and Continua, 82(1), 41–72. https://doi.org/10.32604/CMC.2024.058036
Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Alaya Cheikh, F., Hijji, M., Muhammad, K. & Rodrigues, J. J. P. C. (2023). A comprehensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria Engineering Journal, 68, 817–840. https://doi.org/10.1016/J.AEJ.2023.01.017
Sanli, A. T. & Saran, M. (2024). Application of a Voting-Based Ensemble Method for Recognizing Seven Basic Emotions in Real-Time Webcam Video Images. 2024 IEEE 9th International Conference for Convergence in Technology, I2CT 2024. https://doi.org/10.1109/I2CT61223.2024.10543506
Saxena, S., Tripathi, S. & Tsb, S. (2020). Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation. Frontiers in Artificial Intelligence and Applications, 332, 115–126. https://doi.org/10.3233/FAIA200773
Sharif, M. S., Afolabi, M. O., Zorto, A. & Elmedany, W. (2022). Enhancement Techniques for Improving Facial Recognition Performance in Convolutional Neural Networks. 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022, 494–499. https://doi.org/10.1109/3ICT56508.2022.9990811
Sharma, K. P., Nagpal, T., Raja Praveen, K. N., Yadav, A., Tham, J., Bhosle, N., Palla, S. R. & Chauhan, M. (2025). Evaluating MobileNetV2 Architecture for Resource-Efficient Facial Emotion Recognition. National Academy Science Letters 2025, 1–5. https://doi.org/10.1007/S40009-025-01671-W
Shehu, H. A., Browne, W. N. & Eisenbarth, H. (2025). Emotion categorization from facial expressions: A review of datasets, methods, and research directions. Neurocomputing, 624, 129367. https://doi.org/10.1016/J.NEUCOM.2025.129367
Singh, E. & Nand, P. (2024). Efficient Multi-Class Facial Emotion Recognition using YOLOv9: A Deep Learning Approach for Real-Time Applications. International Journal of Performability Engineering, 20(9), 581. https://doi.org/10.23940/IJPE.24.09.P6.581590
Srivastava, S., Ali, P., Yadav, S., Khan, A. & Pandey, P. (2025). Sign Ease: AI-Based Gesture Recognition. 2025 International Conference on Pervasive Computational Technologies, ICPCT 2025, 645–649. https://doi.org/10.1109/ICPCT64145.2025.10940959
Tutuianu, G. I., Liu, Y., Alamäki, A. & Kauttonen, J. (2023). Benchmarking Deep Facial Expression Recognition: An Extensive Protocol with Balanced Dataset in the Wild. https://arxiv.org/pdf/2311.02910
Wang, Q., Yan, X. & Wang, Y. (2023). Research on deep learning-based facial expression recognition and its application in online learning state monitoring. Https://Doi.Org/10.1117/12.3005824, 12799, 459–464. https://doi.org/10.1117/12.3005824
Wang, Y., Jiang, P., Wang, C. & Hachisuka, S. (2024). Facial Recognition: Decoding Emotions in Online Collaboration. 2024 12th International Conference on Information and Education Technology, ICIET 2024, 333–337. https://doi.org/10.1109/ICIET60671.2024.10542815
Zhang, Z., Fort, J. M. & Giménez Mateu, L. (2023). Facial expression recognition in virtual reality environments: challenges and opportunities. Frontiers in Psychology, 14, 1280136. https://doi.org/10.3389/FPSYG.2023.1280136/BIBTEX
Zhe, J., Ouyang, L., Pan, S.-T. & Wu, H.-J. (2025). FPGA Chip Design of Sensors for Emotion Detection Based on Consecutive Facial Images by Combining CNN and LSTM. Electronics 2025, Vol. 14, Page 3250, 14(16), 3250. https://doi.org/10.3390/ELECTRONICS14163250
Zhu, Q., Zhuang, H., Zhao, M., Xu, S. & Meng, R. (2024). A study on expression recognition based on improved mobilenetV2 network. Scientific Reports, 14(1), 8121. https://doi.org/10.1038/S41598-024-58736-X
