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Machine Learning-Based Facial Recognition for Video Surveillance Systems
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Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model's performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems.
Keywords
Facial Recognition, Machine Learning, Video Surveillance, Deep Learning, Computer Vision
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- G. Guo and N. Zhang, “A Survey on Deep Learning based Face Recognition”, Computer Vision and Image Understanding, Vol. 189, pp. 102805-102813, 2019.
- P.S. Prasad and H.V. Ramana Rao, “Deep Learning based Representation for Face Recognition”, Proceedings of International Conference on Communications and Cyber Physical Engineering, pp. 419-424, 2019.
- M. Masud, S. Ibrahim and M.S. Hossain, “Deep Learning-Based Intelligent Face Recognition in IoT-Cloud Environment”, Computer Communications, Vol. 152, pp. 215-222, 2020.
- T. Akter, S.A. Alyami and M.A. Moni, “Improved Transfer-Learning-based Facial Recognition Framework to Detect Autistic Children at an Early Stage”, Brain Sciences, Vol. 11, No. 6, pp. 734-739, 2021.
- H. Sikkandar and R. Thiyagarajan, “Deep Learning based Facial Expression Recognition using Improved Cat Swarm Optimization”, Journal of Ambient Intelligence and Humanized Computing, Vol. 12, pp. 3037-3053, 2021.
- M.K. Chowdary and D.J. Hemanth, “Deep Learning-Based Facial Emotion Recognition for Human-Computer Interaction Applications”, Neural Computing and Applications, Vol. 78, pp. 1-18, 2021.
- G. Oh, S. Lee and S. Lim, “DRER: Deep Learning Based Driver’s Real Emotion Recognizer”, Sensors, Vol. 21, No. 6, pp. 2166-2175, 2021.
- Y. Mehta and E. Cambria, “Recent Trends in Deep Learning Based Personality Detection”, Artificial Intelligence Review, Vol. 53, pp. 2313-2339, 2020.
- H.C. Kaskavalci and S. Goren, “A Deep Learning based Distributed Smart Surveillance Architecture using Edge and Cloud Computing”, Proceedings of International Conference on Deep Learning and Machine Learning in Emerging Applications, pp. 1-6, 2019.
- M. Rajalakshmi, V. Arunprasad and C. Karthik, “Machine Learning for Modeling and Control of Industrial Clarifier Process”, Intelligent Automation and Soft Computing, Vol. 32, No. 1, pp. 1-12, 2022.
- G. Sunitha, S. Hemalatha and V. Kumar, “Intelligent Deep Learning based Ethnicity Recognition and Classification using Facial Images”, Image and Vision Computing, Vol. 121, pp. 104404-104416, 2022.
- S.I. Serengil and A. Ozpinar, “Lightface: A Hybrid Deep Face Recognition Framework”, Proceedings of International Conference on Innovations in Intelligent Systems and Applications, pp. 1-5, 2020.
- Q. Cao, W. Zhang and Y. Zhu, “Deep Learning-Based Classification of the Polar Emotions of MOE-Style Cartoon Pictures”, Tsinghua Science and Technology, Vol. 26, No. 3, pp. 275-286, 2020.
- A.G. Ismaeel, K. Janardhanan, M. Sankar and A.H. Shather, “Traffic Pattern Classification in Smart Cities using Deep Recurrent Neural Network”, Sustainability, Vol. 15, No. 19, pp. 14522-14531, 2023.
- M. Elhoseny, M.M. Selim and K. Shankar, “Optimal Deep Learning based Convolution Neural Network for Digital Forensics Face Sketch Synthesis in Internet of Things (IoT)”, International Journal of Machine Learning and Cybernetics, Vol. 12, pp. 3249-3260, 2021.
- L.A. Kumar and I.M. Wartana, “Deep Learning based Assistive Technology on Audio Visual Speech Recognition for Hearing Impaired”, International Journal of Cognitive Computing in Engineering, Vol. 3, pp. 24-30, 2022.
- J. Perumal and S.J.N. Kumar, “Categorical Data Clustering using Meta Heuristic Link-Based Ensemble Method: Data Clustering using Soft Computing Techniques”, Proceedings of International Conference on Dynamics of Swarm Intelligence Health Analysis for the Next Generation, pp. 226-238, 2023.
- V.K. Gunjan, Y. Vijayalata and S. Valli, “Machine Learning and Cloud-Based Knowledge Graphs to Recognize Suicidal Mental Tendencies”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-9, 2022.
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