Enhancing Surveillance with Machine and Deep Learning-Based Facial Recognition Model: A Proposed Approach for Identification
DOI:
https://doi.org/10.31272/jeasd.2633Keywords:
Custom Deep Learning, Facial Recognition, Linear Discriminant, Multilayer Perceptron, Support Vector MachineAbstract
There is limited understanding and utilization of facial recognition models in surveillance. This work addresses the underutilization of facial recognition Models in surveillance contexts. A Model that leverages its proposed facial recognition technology to monitor and locate individuals in real-time video streams and dataset images. The model begins with an initial dataset containing images of specific individuals, such as university professors, missing persons, or criminals. These images extract essential facial attributes for training models capable of identifying individuals in live video recordings. Upon a successful match, the model identifies individuals and tracks their movements using surveillance cameras. A primary objective of this work is to integrate the proposed model seamlessly with the current surveillance infrastructure, minimizing operational costs and disruptions. The work employs two main artificial intelligence approaches: Support Vector Machine achieved an accuracy of 85.33%, demonstrating effective facial recognition compared to the Multilayer Perceptron with 89.0% accuracy. Additionally, Linear Discriminant Analysis achieved the highest classification accuracy at 87.66%. Furthermore, our custom deep learning model demonstrated exceptional accuracy, ranging between 99.5% and 99.8%, showcasing significant advancements over existing methodologies.
References
Y. Yao, “Research on facial recognition system based on deep learning,” Applied and Computational Engineering, vol. 34, no. 7, pp. 232–236, 2024, doi: https://doi.org/10.54254/2755-2721/34/20230332.
L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J Big Data, vol. 8, no. 53, pp. 1–74, 2021, doi: https://doi.org/10.1186/s40537-021-00444-8.
L. Qinjun, C. Tianwei, Z. Yan, and W. Yuying, “Facial Recognition Technology: A Comprehensive Overview,” Academic Journal of Computing & Information Science, vol. 6, no. 7, pp. 15–26, 2023, doi: https://doi.org/10.25236/AJCIS.2023.060703.
Z. B. Lahaw, D. Essaidani, and H. Seddik, “Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms,” in 2018 41st International Conference on Telecommunications and Signal Processing, TSP 2018, 2018, pp. 413–417. doi: https://doi.org/10.1109/TSP.2018.8441452.
R. C. Damale and B. V. Pathak, “Face Recognition Based Attendance System Using Machine Learning Algorithms,” in Proceedings of the 2nd International Conference on Intelligent Computing and Control Systems, ICICCS 2018, 2019, pp. 414–419. doi: https://doi.org/10.1109/ICCONS.2018.8662938.
A. Adouani, W. M. Ben Henia, and Z. Lachiri, “Comparison of Haar-like, HOG and LBP approaches for face detection in video sequences,” in 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), 2019, pp. 266–271. doi: https://doi.org/10.1109/SSD.2019.8893214.
N. Sabri et al., “A comparison of face detection classifier using facial geometry distance measure,” in 2018 9th IEEE Control and System Graduate Research Colloquium, ICSGRC 2018 - Proceeding, 2019, pp. 116–120. doi: https://doi.org/10.1109/ICSGRC.2018.8657592.
S. Sharma, M. Bhatt, and P. Sharma, “Face recognition system using machine learning algorithm,” Proceedings of the 5th International Conference on Communication and Electronics Systems, ICCES 2020, no. Icces, pp. 1162–1168, 2020, doi: https://doi.org/10.1109/ICCES48766.2020.09137850.
M. J. Al_Dujaili, H. T. H. Salim ALRikabi, and I. R. Niama ALRubeei, “Gender Recognition of Human from Face Images Using Multi-Class Support Vector Machine (SVM) Classifiers.,” International Journal of Interactive Mobile Technologies, vol. 17, no. 8, 2023, https://doi.org/10.3991/ijim.v17i08.39163.
J. I.-Z. Chen, C.-Y. Lee, and W.-C. Lo, “The Pre-Processing of WT Blade Images by SS and Bilateral Filter with Machine Learning Frameworks,” J Mar Sci Technol, vol. 31, no. 4, p. 16, 2023, doi: https://doi.org/10.51400/2709-6998.2724.
S. H. Fatima, A. Munir, and S. T. Hussain, “Image denoising using difference classifier and trimmed global mean filter adaptive approach,” Vis Comput, vol. 40, no. 8, pp. 1–13, 2023, doi: https://doi.org/10.1007/s00371-023-03106-3.
H. H. Afshari, S. A. Gadsden, and S. Habibi, “Gaussian filters for parameter and state estimation: A general review of theory and recent trends,” Signal Processing, vol. 135, no. 1, pp. 218–238, 2017, doi: https://doi.org/10.1016/j.sigpro.2017.01.001.
G. George, R. M. Oommen, S. Shelly, S. S. Philipose, and A. M. Varghese, "A survey on various median filtering techniques for removal of impulse noise from a digital image," in 2018 Conference on Emerging Devices and Smart Systems (ICEDSS), IEEE, 2018, pp. 235–238. doi: https://doi.org/10.1109/ICEDSS.2018.8544273.
R. Li, “An optimal algorithm for bilateral filtering,” in Journal of Physics: Conference Series, IOP Publishing, 2023, p. 12030. doi: https://doi.org/10.1088/1742-6596/2634/1/012030.
B.-H. Chen, Y.-S. Tseng, and J.-L. Yin, “Gaussian-adaptive bilateral filter,” IEEE Signal Process Lett, vol. 27, no. 1, pp. 1670–1674, 2020, doi: https://doi.org/10.1109/LSP.2020.3024990.
P. Musa, F. Al Rafi, and M. Lamsani, "A Review: Contrast-Limited Adaptive Histogram Equalization (CLAHE) methods to help the application of face Recognition," in 2018 Third International Conference on Informatics and Computing (ICIC), IEEE, 2018, pp. 1–6. doi: https://doi.org/10.1109/IAC.2018.8780492.
K. Meena, “LOCAL BINARY PATTERNS AND ITS,” in IEEE-International Conference on Recent Trends in Information Technology, 2020, pp. 3–5. doi: https://doi.org/10.1109/ICRTIT.2011.5972286.
H. Taud and J. F. Mas, “Multilayer perceptron (MLP),” in Geomatic approaches for modeling land change scenarios, Springer, 2018, pp. 451–455.Book. https://www.springerprofessional.de/en/geomatic-approaches-for-modeling-land-change-scenarios/15171360?tocPage=1
F. Zhu, J. Gao, J. Yang, and N. Ye, “Neighborhood linear discriminant analysis,” Pattern Recognit, vol. 123, no. 1, p. 108422, 2022, doi: https://doi.org/10.1016/j.patcog.2021.108422.
X. Yan and H. Zhu, “A novel robust support vector machine classifier with feature mapping,” Knowl Based Syst, vol. 257, no. 3, p. 109928, 2022, doi: https://doi.org/10.1016/j.knosys.2022.109928.
X. Ding, J. Liu, F. Yang, and J. Cao, “Random radial basis function kernel-based support vector machine,” J Franklin Inst, vol. 358, no. 18, pp. 10121–10140, 2021, doi: https://doi.org/10.1016/j.jfranklin.2021.10.005.
S. Al Ghifari and H. Studiawan, “Image Splicing Localization Using Superpixel and Wavelet Mean Squared Error,” in 2023 International Conference on Information Technology (ICIT), IEEE, 2023, pp. 593–598. doi: https://doi.org/10.1109/ICIT58056.2023.10226015.
M. Krichen, “Convolutional neural networks: A survey,” Computers, vol. 12, no. 8, p. 151, 2023, doi: https://doi.org/10.3390/computers12080151.
A. S. Mohammad, “Multi-Modal Ocular Recognition in Presence of Occlusion in Mobile Devices,” 2018. https://hdl.handle.net/10355/69672
R. Chauhan, K. K. Ghanshala, and R. C. Joshi, “Convolutional neural network (CNN) for image detection and recognition,” in 2018 first international conference on secure cyber computing and communication (ICSCCC), IEEE, 2018, pp. 278–282. doi: https://doi.org/10.1109/ICSCCC.2018.8703316.
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Copyright (c) 2025 Husham Salam Saeed , Muhammad Hassan Fares (Author)
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