(Hassan II University, Morocco)(2) Salah-Eddine Mansour
(Hassan II University, Morocco)*corresponding author
AbstractOnline education, especially post-COVID, faces the challenge of maintaining student engagement, particularly at the college level. A key factor in effective learning is understanding students’ emotional states, as they influence comprehension and participation. To address this, we propose an intelligent system that classifies students’ emotions by analyzing facial expressions, allowing teachers to adapt their methods in real-time. Our system utilizes the Learning Focal Point algorithm to improve emotion classification accuracy, focusing on key facial regions related to emotional expressions. The methodology involves preprocessing facial images, extracting features, and classifying emotions using the algorithm. Trained on a diverse dataset, the system performs well under various conditions, with a classification accuracy of 94% based on a well-known database. Although the system shows significant improvements over traditional methods, factors like image quality and internet connection can impact accuracy in realworld applications. Ultimately, our approach enhances remote learning by providing real-time emotional feedback, fostering a more responsive and student-centered environment.
KeywordsEmotion Detection; Affective Computing; Facial Expression Recognition; Sentiment Analysis; Emotion Modeling
|
DOIhttps://doi.org/10.31763/ijrcs.v5i2.1827 |
Article metrics10.31763/ijrcs.v5i2.1827 Abstract views : 109 | PDF views : 17 |
Cite |
Full Text Download
|
References
[1] W. A. D. Strasse, D. P. Campos, J. Mendes, C. J. A. Mendonça, J. F. Soni and P. Nohama, “Image Acquisition Protocol by Infrared Medical Thermography in Diaphyseal Tibial Injuries in a Patient Diagnosed with Pseudarthrosis – A Case Study,” 2021 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1-4, 2021, https://doi.org/10.1109/GMEPE/PAHCE50215.2021.9434847.
[2] S. Pan et al., “Land-cover classification of multispectral LiDAR data using CNN with optimized hyper parameters,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 166, pp. 241–254, 2020, https://doi.org/10.1016/j.isprsjprs.2020.05.022.
[3] R. B. Mancilla, C. Daul, J. G. Martinez, L. L. Salas, D. Wolf, and A. V. Hernandez, “A Quantitative Method for the Detection of Temperature Differences on the Sole of the Foot in Diabetic Patients,” in 2021 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1–5, 2021, https://doi.org/10.1109/GMEPE/PAHCE50215.2021.9434852.
[4] R. Bayareh Mancilla, C. Daul, J. Gutierrez Martinez, A. Vera Hernandez, D. Wolf, and L. Leija Salas, “Detection of Sore-risk Regions on the Foot Sole with Digital Image Processing and Passive Thermography in Diabetic Patients,” in 2020 17th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6, 2020, https://doi.org/10.1109/CCE50788.2020.9299144.
[5] C. B. Goncalves, J. R. Souza, and H. Fernandes, “Classification of static infrared images using pre trained CNN for breast cancer detection,” in 2021 IEEE 34th International Symposium on Computer- Based Medical Systems (CBMS), pp. 101–106, 2021, https://doi.org/10.1109/CBMS52027.2021.00094.
[6] K. Rassels and P. French, “Accurate Body Temperature Measurement of a Neonate Using Thermography Technology,” in 2021 Smart Systems Integration (SSI), pp. 1–5, 2021, https://doi.org/10.1109/SSI52265.2021.9467024.
[7] M. A. Farooq and P. Corcoran, “Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images,” in 2020 31st Irish Signals and Systems Conference (ISSC), pp. 1–6, 2020, https://doi.org/10.1109/ISSC49989.2020.9180164.
[8] Y. Dang, C. Benza??d, B. Yang, T. Taleb and Y. Shen, “Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs,” in IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25068-25085, 2022, https://doi.org/10.1109/JIOT.2022.3195320.
[9] P. Rajadanuraks, S. Suranuntchai, S. Pechprasarn, and T. Treebupachatsakul, “Performance Comparison for Different Neural Network Architectures for Chest X-Ray Image Classification,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 49–53, 2021, https://doi.org/10.1109/ICEAST52143.2021.9426289.
[10] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, “Handwritten Character Recognition from Images using CNN-ECOC,” Procedia Computer Science, vol. 167, pp. 2403–2409, 2020, https://doi.org/10.1016/j.procs.2020.03.293.
[11] A. M. Fangoh and S. Selim, “Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images,” in 2020 15th International Conference on Computer Engineering and Systems (ICCES), pp. 1–7, 2020, https://doi.org/10.1109/ICCES51560.2020.9334600.
[12] N. Nafi’iyah and E. Setyati, “Lung X-Ray Image Enhancement to Identify Pneumonia with CNN,” in 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), pp. 421–426, 2021, https://doi.org/10.1109/EIConCIT50028.2021.9431856.
[13] P. Naveen and B. Diwan, “Pre-trained VGG-16 with CNN Architecture to Classify X-Rays Images into Normal or Pneumonia,” in 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 102–105, 2021, https://doi.org/10.1109/ESCI50559.2021.9396997.
[14] Y. Ge et al., ``Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique,” in IEEE Transactions on Biomedical Engineering, vol. 68, no. 6, pp. 1751-1758, 2021, https://doi.org/10.1109/TBME.2020.3011119.
[15] G. Labhane, R. Pansare, S. Maheshwari, R. Tiwari, and A. Shukla, “Detection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning,” in 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 85–92, 2020, https://doi.org/10.1109/ICETCE48199.2020.9091755.
[16] Y. Luo, S. Majoe, J. Kui, H. Qi, K. Pushparajah and K. Rhode, “Ultra-Dense Denoising Network: Application to Cardiac Catheter-Based X-Ray Procedures,” in IEEE Transactions on Biomedical Engineering, vol. 68, no. 9, pp. 2626-2636, 2021, https://doi.org/10.1109/TBME.2020.3041571.
[17] S. Tewari, U. Agrawal, S. Verma, S. Kumar, and S. Jeevaraj, “Ensemble Model for COVID-19 Detection from Chest X-ray Scans using Image Segmentation, Fuzzy Color and Stacking Approaches,” in 2020 IEEE 4th Conference on Information & Communication Technology (CICT), pp. 1–6, 2020, https://doi.org/10.1109/CICT51604.2020.9312076.
[18] G. Aparna, S. Gowri, R. Bharathi, V. J. S, J. J and A. P, “COVID-19 Prediction using X-Ray Images,” 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 903-908, 2021, https://doi.org/10.1109/ICOEI51242.2021.9452740.
[19] T. Hassan, M. Bettayeb, S. Akcay, S. Khan, M. Bennamoun, and N. Werghi, “Detecting Prohibited Items in X-Ray Images: A Contour Proposal Learning Approach,” in 2020 IEEE International Conference on Image Processing (ICIP), pp. 2016–2020, 2020, https://doi.org/10.1109/ICIP40778.2020.9190711.
[20] ?. Güven and F. ?im?ir, “Demand Forecasting with Color Parameter in Retail Apparel Industry Using Artificial Neural Networks (ANN) and Support Vector Machines (SVM) Methods,” Computers & Industrial Engineering, vol. 147, p. 106678, 2020, https://doi.org/10.1016/j.cie.2020.106678.
[21] S. H. G. Silva et al., “Soil Texture Prediction in Tropical Soils: A Portable X-Ray Fluorescence Spectrometry Approach,” Geoderma, vol. 362, p. 114136, 2020, https://doi.org/10.1016/j.geoderma.2019.114136.
[22] S. Parveen and K. B. Khan, “Detection and Classification of Pneumonia in Chest X-ray Images by Supervised Learning,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), pp. 1–5, 2020, https://doi.org/10.1109/INMIC50486.2020.9318118.
[23] Y. Dang, C. Benzaid, B. Yang, T. Taleb, and Y. Shen, “DeepEnsemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs,” IEEE Internet of Things Journal, vol. 9, no. 24, pp. 25068–25085, 2022, https://doi.org/10.1109/JIOT.2022.3195320.
[24] P. Rajadanuraks, S. Suranuntchai, S. Pechprasarn, and T. Treebupachatsakul, “Performance Comparison for Different Neural Network Architectures for chest X-Ray Image Classification,” in 2021 7th International Conference on Engineering, Applied Sciences and Technology (ICEAST), pp. 49–53, 2021, https://doi.org/10.1109/ICEAST52143.2021.9426289.
[25] M. B. Bora, D. Daimary, K. Amitab, and D. Kandar, “Handwritten Character Recognition from Images using CNN-ECOC,” Procedia Computer Science, vol. 167, pp. 2403–2409, 2020, https://doi.org/10.1016/j.procs.2020.03.293.
[26] A. M. Fangoh and S. Selim, “Using CNN-XGBoost Deep Networks for COVID-19 Detection in Chest X-ray Images,” in 2020 15th International Conference on Computer Engineering and Systems (ICCES), pp. 1–7, 2020, https://doi.org/10.1109/ICCES51560.2020.9334600.
[27] N. Nafi’iyah and E. Setyati, “Lung X-Ray Image Enhancement to Identify Pneumonia with CNN,” in 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT), pp. 421–426, 2021, https://doi.org/10.1109/EIConCIT50028.2021.9431856.
[28] P. Naveen and B. Diwan, “Pre-trained VGG-16 with CNN Architecture to classify X-Rays images into Normal or Pneumonia,” in 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), pp. 102–105, 2021, https://doi.org/10.1109/ESCI50559.2021.9396997.
[29] Y. Ge et al., “Enhancing the X-Ray Differential Phase Contrast Image Quality With Deep Learning Technique,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 6, pp. 1751–1758, 2021, https://doi.org/10.1109/TBME.2020.3011119.
[30] G. Labhane, R. Pansare, S. Maheshwari, R. Tiwari, and A. Shukla, “Detection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning,” in 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE), pp. 85–92, 2020, https://doi.org/10.1109/ICETCE48199.2020.9091755.
[31] Y. Luo, S. Majoe, J. Kui, H. Qi, K. Pushparajah, and K. Rhode, “Ultra-Dense Denoising Network: Application to Cardiac Catheter-Based X-Ray Procedures,” IEEE Transactions on Biomedical Engineering, vol. 68, no. 9, pp. 2626–2636, 2021, https://doi.org/10.1109/TBME.2020.3041571.
[32] S. Tewari, U. Agrawal, S. Verma, S. Kumar, and S. Jeevaraj, “Ensemble Model for COVID-19 detection from chest X-ray Scans using Image Segmentation, Fuzzy Color and Stacking Approaches,” in 2020 IEEE 4th Conference on Information & Communication Technology (CICT), pp. 1–6, 2020, https://doi.org/10.1109/CICT51604.2020.9312076.
[33] G. Aparna, S. Gowri, R. Bharathi, V. J. S, J. J, and A. P, “COVID-19 Prediction using X-Ray Images,” in 2021 5th International Conference on Trends in Electronics and Informatics (ICOEI), pp. 903–908, 2021, https://doi.org/10.1109/ICOEI51242.2021.9452740.
[34] T. Hassan, M. Bettayeb, S. Akcay, S. Khan, M. Bennamoun, and N. Werghi, “Detecting Prohibited Items in X-Ray Images: a Contour Proposal Learning Approach,” in 2020 IEEE International Conference on Image Processing (ICIP), pp. 2016–2020, 2020, https://doi.org/10.1109/ICIP40778.2020.9190711.
[35] ?. Güven and F. ?im?ir, “Demand forecasting with color parameter in retail apparel industry using artificial neural networks (ANN) and support vector machines (SVM) methods,” Computers & Industrial Engineering, vol. 147, p. 106678, 2020, https://doi.org/10.1016/j.cie.2020.106678.
[36] S. H. G. Silva et al., “Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach,” Geoderma, vol. 362, p. 114136, 2020, https://doi.org/10.1016/j.geoderma.2019.114136.
[37] S. Parveen and K. B. Khan, “Detection and classification of pneumonia in chest X-ray images by supervised learning,” in 2020 IEEE 23rd International Multitopic Conference (INMIC), pp. 1–5, 2020, https://doi.org/10.1109/INMIC50486.2020.9318118.
[38] Z. Mei, K. Ivanov, G. Zhao, Y. Wu, M. Liu, and L. Wang, “Foot type classification using sensor-enabled footwear and 1D-CNN,” Measurement, vol. 165, p. 108184, 2020, https://doi.org/10.1016/j.measurement.2020.108184.
[39] J. Bonnard, K. Abdelouahab, M. Pelcat, and F. Berry, “On building a CNN-based multi-view smart camera for real-time object detection,” Microprocessors and Microsystems, vol. 77, p. 103177, 2020, https://doi.org/10.1016/j.micpro.2020.103177.
[40] L. M. Schliephake, I. Trempler, M. A. Roehe, N. Heins, and R. I. Schubotz, “Positive and negative prediction error signals to violated expectations of face and place stimuli distinctively activate FFA and PPA,” NeuroImage, vol. 236, p. 118028, 2021, https://doi.org/10.1016/j.neuroimage.2021.118028.
[41] A. M. Muir, A. C. Eberhard, M. S. Walker, A. Bennion, M. South, and M. J. Larson, “Dissociating the effect of reward uncertainty and timing uncertainty on neural indices of reward prediction errors: A reward positivity (RewP) event-related potential (ERP) study,” Biological Psychology, vol. 163, p. 108121, 2021, https://doi.org/10.1016/j.biopsycho.2021.108121.
[42] V. N. Almeida, “Neurophysiological basis of the N400 deflection, from Mismatch Negativity to Semantic Prediction Potentials and late positive components,” International Journal of Psychophysiology, vol. 166, pp. 134–150, 2021, https://doi.org/10.1016/j.ijpsycho.2021.06.001.
[43] E. Rawls and C. Lamm, “The aversion positivity: Mediofrontal cortical potentials reflect parametric aversive prediction errors and drive behavioral modification following negative reinforcement,” Cortex, vol. 140, pp. 26–39, 2021, https://doi.org/10.1016/j.cortex.2021.03.012.
[44] S. E. Mansour, A. Sakhi, L. Kzaz, O. Tali, and A. Sekkaki, “Focal Point of Learning,” in 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0522–0526, 2021, https://doi.org/10.1109/UEMCON53757.2021.9666524.
[45] A. Sakhi, S.-E. Mansour, A. Sekkaki, and K. Benzidane, “The Use of the AI to Classify the Emotion Intelligence (EI) Students,” in 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 0148–0152, 2022, https://doi.org/10.1109/IEMCON56893.2022.9946581.
[46] S. E. Mansour, A. Sakhi, L. Kzaz, A. Erroutbi, and A. Sekkaki, “Electronic device for acquiring images of sardine cans,” in 2022 IEEE World AI IoT Congress (AIIoT), pp. 471–475, 2022, https://doi.org/10.1109/AIIoT54504.2022.9817260.
[47] S. Eddine Mansour, A. Sakhi, L. Kzaz, O. Tali, and A. Sekkaki, “Design, security and implementation of learning focal point algorithm in a docker container,” IJEECS, vol. 33, no. 1, p. 416, 2024, https://doi.org/10.11591/ijeecs.v33.i1.pp416-424.
[48] S.-E. Mansour, A. Sakhi, L. Kzaz, and A. Sekkaki, “Enhancing Security Mechanisms for IoT-Fog Networks,” Journal of Robotics and Control, vol. 5, no. 1, pp. 152–159, 2024, https://doi.org/10.18196/jrc.v5i1.20745.
[49] A. Sakhi, S.-E. Mansour, and A. Sekkaki, “Using Learning Focal Point Algorithm to Classify Emotional Intelligence,” Journal of Robotics and Control, vol. 5, no. 1, pp. 263–270, 2024, https://doi.org/10.18196/jrc.v5i1.20895.
[50] S. E. Mansour, A. Sakhi, L. Kzaz, and A. Sekkaki, “Leveraging the learning focal point algorithm for emotional intelligence,” International Journal of Reconfigurable and Embedded Systems (IJRES), vol. 13, no. 3, p. 767, 2024, https://doi.org/10.11591/ijres.v13.i3.pp767-773.
[51] S. E. Mansour, A. Sakhi, “Detection of Sealing Defects in Canned Sardines Using Local Binary Pattern and Perceptron Techniques for Enhanced Quality Control,” International Journal of Robotics and Control Systems, vol. 5, no. 1, p. 585, 2025, https://doi.org/10.31763/ijrcs.v5i1.1737.pp585-598
[52] S. E. Mansour, A. Sakhi, “Leveraging LFP Architecture for Pneumothorax Detection in Chest X-rays,” Journal of Robotics and Control, vol. 6, no. 1, pp. 336–344, 2025, https://doi.org/10.18196/jrc.v6i1.25107.
[53] M. M. Odeh et al., “A prediction model of risk factors for complications among SARS-CoV2 positive patients: Cases from Jordan,” Journal of Infection and Public Health, vol. 14, no. 6, pp. 689–695, 2021, https://doi.org/10.1016/j.jiph.2021.02.010.
[54] A. Sonawat, H.-M. Yang, and J.-H. Kim, “Experimental study of positive displacement hydraulic turbine at various temperatures and development of empirical co-relation for flowrate prediction,” Renewable Energy, vol. 172, pp. 1293–1300, 2021, https://doi.org/10.1016/j.renene.2021.03.118.
[55] Y. Ochi et al., “Validation of the Kumamoto criteria for prediction of 99m technetium pyrophosphate scintigraphy positivity as a strategy for diagnosis of transthyretin cardiac amyloidosis: A retrospective cohort study in Kochi,” Journal of Cardiology, vol. 77, no. 2, pp. 124–130, 2021, https://doi.org/10.1016/j.jjcc.2020.06.019.
[56] D. L. Pinto et al., “Image feature extraction via local binary patterns for marbling score classification in beef cattle using tree-based algorithms,” Livestock Science, vol. 267, p. 105152, 2023, https://doi.org/10.1016/j.livsci.2022.105152.
[57] N. Guillou and G. Chapalain, “Machine learning methods applied to sea level predictions in the upper part of a tidal estuary,” Oceanologia, vol. 63, no. 4, pp. 531–544, 2021, https://doi.org/10.1016/j.oceano.2021.07.003.
Refbacks
- There are currently no refbacks.
Copyright (c) 2025 Kaoutar Elbennani, Salah-Eddine Mansour, Abdelhak Sakhi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia