2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 130-141, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Dhiaa M. Abed 1 , Awab Qasim Karamanj 2 , Thura J. Mohammed 3 , Saja B. Attallah 4 , Abusnina M. Mukhtar 5 *
Doi: https://doi.org/10.54216/JISIoT.180210
Facial Expression Recognition (FER) is a vital aspect of human-computer interaction with applications in healthcare, education security, and affective computing. Even with the success of deep learning, generalizability, interpretability, and efficiency of most systems, especially in uncontrolled settings, are still problematic. In this study, we propose an enhanced feature extraction technique based on Histograms of Oriented Gradient (HOG) where the central difference operator, not the conventional forward difference, used for gradient estimation. The modification enhances the accuracy of gradients, reduces truncation error, and leads to more stable facial feature descriptors. The enhanced HOG is tested on five popular datasets, CK+, JAFFE, MMI, ExpW, and AffectNet, using three traditional Machine Learning (ML) classifiers: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). Experimental results indicate uniform accuracy enhancements across all the classifiers and datasets, with improvements spiking to 7%–10% and recall and F1-score also witnessing marked increases. In this study, RF registered the maximum accuracy, 97.94%, on CK+ and 95.48% on AffectNet, hence solidifying its stability and dependability. This study shows how well mathematical optimization works with classical ML for FER. The approach we suggest provides an easy-to-understand, small, and quick alternative to deep models, making it perfect for real-time and resource-limited applications.
Facial Expression Recognition , Improved HOG , Central Difference Gradient , Machine Learning , Feature Extraction , Affective Computing
[1] M. Shokry, A. I. Awad, M. K. Abd-Ellah, and A. A. M. Khalaf, “Systematic survey of advanced metering infrastructure security: Vulnerabilities, attacks, countermeasures, and future vision,” Future Gener. Comput. Syst., vol. 136, pp. 358–377, 2022, doi: 10.1016/j.future.2022.06.013.
[2] M. Sajjad et al., “Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities,” Future Gener. Comput. Syst., vol. 108, pp. 995–1007, 2020, doi: 10.1016/j.future.2017.11.013.
[3] X. Lv, M. Su, and Z. Wang, “Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems,” Microprocess. Microsyst., p. 104034, 2021, doi: 10.1016/j.micpro.2021.104034.
[4] S. Barra, S. Hossain, C. Pero, and S. Umer, “A Facial Expression Recognition Approach for Social IoT Frameworks,” Big Data Res., vol. 30, p. 100353, 2022, doi: 10.1016/j.bdr.2022.100353.
[5] M. A. P. Chamikara, P. Bertok, I. Khalil, D. Liu, and S. Camtepe, “Privacy Preserving Face Recognition Utilizing Differential Privacy,” Comput. Secur., vol. 97, p. 101951, 2020, doi: 10.1016/j.cose.2020.101951.
[6] X. Zhao, J. Li, W. Liu, J. Zhang, and Y. Li, “Design of the sleeping aid system based on face recognition,” Ad Hoc Netw., vol. 99, p. 102070, 2020, doi: 10.1016/j.adhoc.2019.102070.
[7] S. Demir, S. Key, T. Tuncer, and S. Dogan, “An exemplar pyramid feature extraction based humerus fracture classification method,” Med. Hypotheses, vol. 140, p. 109663, 2020, doi: 10.1016/j.mehy.2020.109663.
[8] A. Amirkhani, M. P. Karimi, and A. Banitalebi-Dehkordi, “A survey on adversarial attacks and defenses for object detection and their applications in autonomous vehicles,” Vis. Comput., vol. 39, no. 11, pp. 5293–5307, 2023, doi: 10.1007/s00371-022-02660-6.
[9] S. M. Imran, S. M. M. Rahman, and D. Hatzinakos, “Differential components of discriminative 2D Gaussian–Hermite moments for recognition of facial expressions,” Pattern Recognit., vol. 56, pp. 100–115, 2016, doi: 10.1016/j.patcog.2016.03.006.
[10] 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, 2023, doi: 10.1007/s00521-021-06012-8.
[11] R. Sadik, S. Anwar, and L. Reza, “AutismNet: Recognition of Autism Spectrum Disorder from Facial Expressions using MobileNet Architecture,” in Proc. Int. J. Adv. Trends Comput. Sci. Eng., 2021, doi: 10.30534/ijatcse/2021/471012021.
[12] D. Zhang and Q. Tian, “A Novel Fuzzy Optimized CNN-RNN Method for Facial Expression Recognition,” 2021, arXiv: 2112.14513.
[13] T. Podder, D. Bhattacharya, and A. Majumdar, “Dew Computing-Inspired Mental Health Monitoring System Framework Powered by a Lightweight CNN,” in Disruptive Technologies for Big Data and Cloud Applications. Singapore: Springer, 2022, pp. 309–319.
[14] H. B. U. Haq, W. Akram, M. N. Irshad, A. Kosar, and M. Abid, “Enhanced Real-Time Facial Expression Recognition Using Deep Learning,” Acadlore Trans. AI Mach. Learn., vol. 3, no. 1, pp. 24–35, 2024, doi: 10.56578/ataiml030103.
[15] M. Ahmed, S. Byreddy, A. Nutakki, L. F. Sikos, and P. Haskell-Dowland, “ECU-IoHT: A dataset for analyzing cyberattacks in Internet of Health Things,” Ad Hoc Netw., vol. 122, p. 102621, 2021, doi: 10.1016/j.adhoc.2021.102621.
[16] M. M. Bapat, S. M. Mali, and V. Karad, “Database Development and Recognition of Facial Expression using Deep Learning,” Int. J. Comput., vol. 23, no. 4, 2023, doi: 10.47839/ijc.23.4.3760.
[17] L. Boussaad and A. Boucetta, “An effective component-based age-invariant face recognition using Discriminant Correlation Analysis,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 5, pp. 1739–1747, 2022, doi: 10.1016/j.jksuci.2020.08.009.
[18] I. S. Badr et al., “Cancellable face recognition based on fractional-order Lorenz chaotic system and Haar wavelet fusion,” Digit. Signal Process., vol. 116, p. 103103, 2021, doi: 10.1016/j.dsp.2021.103103.
[19] I. S. Na et al., “FacialNet: facial emotion recognition for mental health analysis using UNet segmentation with transfer learning model,” Front. Comput. Neurosci., vol. 18, 2024, doi: 10.3389/fncom.2024.1485121.
[20] S. Minaee, M. Minaei, and A. Abdolrashidi, “Deep-emotion: Facial expression recognition using attentional convolutional network,” Sensors, vol. 21, no. 9, p. 3046, 2021, doi: 10.3390/s21093046.
[21] S. Karanwal, “Robust face descriptor in unconstrained environments,” Expert Syst. Appl., vol. 247, p. 123302, 2024, doi: 10.1016/j.eswa.2024.123302.
[22] A. K. Sharma et al., “HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection,” Biomed. Signal Process. Control, vol. 84, p. 104737, 2023, doi: 10.1016/j.bspc.2023.104737.
[23] Z. Ding et al., “Multi-resolution 3D-HOG feature learning method for Alzheimer’s Disease diagnosis,” Comput. Methods Programs Biomed., vol. 214, p. 106574, 2022, doi: 10.1016/j.cmpb.2021.106574.
[24] M. Mebarkia, A. Meraoumia, L. Houam, and S. Khemaissia, “X-ray image analysis for osteoporosis diagnosis: From shallow to deep analysis,” Displays, vol. 76, p. 102343, 2023, doi: 10.1016/j.displa.2022.102343.
[25] A. Sharma, D. P. Yadav, H. Garg, M. Kumar, B. Sharma, and D. Koundal, “Bone Cancer Detection Using Feature Extraction Based Machine Learning Model,” Comput. Math. Methods Med., vol. 2021, p. 7433186, 2021, doi: 10.1155/2021/7433186.
[26] S. Ramachandra and S. Ramachandran, “Region specific and subimage based neighbour gradient feature extraction for robust periocular recognition,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 7961–7973, 2022, doi: 10.1016/j.jksuci.2022.07.013.
[27] D. M. Abed, S. Abdul-Rahman, and S. Mutalib, “Dental segmentation via enhanced YOLOv8 and image processing techniques,” Mesopotamian J. CyberSecurity, vol. 4, no. 3, pp. 189–202, 2024, doi: 10.58496/MJCS/2024/022.
