Fuzzy Enhanced AI Facial Recognition System for Robust and Efficient Identification
Keywords:
Artificial Intelligence, CNN, Face Detection, Facial Recognition, Fuzzy Algorithm, Pattern Classification, Real-Time DetectionAbstract
Facial recognition systems have gained considerable attention in recent years due to their extensive applications in security, surveillance, and biometric authentication. However, traditional facial recognition approaches often face limitations in detecting faces accurately in dynamic environments with variations in illumination, pose, and occlusions. To overcome these challenges, this research proposes an artificial intelligence powered facial recognition system integrated with a fuzzy-based algorithm for efficient face detection and recognition. The proposed system utilizes convolutional neural networks (CNN) for feature extraction, while fuzzy logic principles are applied for handling uncertainties and imprecise facial patterns during the detection phase. The integration of fuzzy rules enhances decision-making capability by efficiently classifying ambiguous facial features under challenging conditions. Experimental evaluations demonstrate that the proposed model outperforms existing conventional models in terms of detection accuracy, response time, and adaptability across diverse datasets. The results validate that the proposed AI-fuzzy integrated facial recognition system ensures robustness and reliability in real-world applications.
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Ali, F., Zaman, H., Ahmed, S., & Khan, M. (2023). AI-driven smart healthcare systems: A review of deep learning-based assistive technologies. Computers in Biology and Medicine, 157, 106728.
Chen, Y., Zhang, Z., & Wang, X. (2022). Intelligent assistive system for visually impaired persons using deep learning models. Artificial Intelligence Review, 55(3), 2189-2210.
Patel, V., Pandya, P., & Shah, P. (2020). AI-enabled wearable devices for assisting visually impaired: A review. Procedia Computer Science, 171, 1070-1077.
Rahman, M., Saha, H., & Kundu, S. (2020). Real-time object detection and feedback system for visually impaired people using deep learning. International Journal of Advanced Computer Science and Applications, 11(7), 434-440.
Ahmed, S., Akhtar, M., & Beg, M. (2021). Hybrid deep learning approach for object detection in assistive technology applications. Journal of Ambient Intelligence and Humanized Computing, 12(6), 6757-6770.
Gupta, P., Singh, S., & Thakur, R. (2022). Deep learning-based voice feedback system for visually impaired people. Wireless Personal Communications, 125(1), 765-781.
Salman Ali Syed, S. Gowthami, S. Chandragandhi, K. Srihari, et al. (2024). Registration based fully optimized melanoma detection using deep forest technique. Biomedical Signal Processing and Control, 93, 106116.
S.S. Saranya, Palagati Anusha, K. Srihari, et al. (2024). Enhanced decision-making in healthcare cloud-edge networks using deep reinforcement and lion optimization algorithm. Biomedical Signal Processing and Control, 92, 105963.
Barakkath Nisha, U., Uma Maheswari, N., Venkatesh, R., & Yasir Abdullah, R. (2014). Robust estimation of incorrect data using relative correlation clustering technique in wireless sensor networks. 2014 International Conference on Communication and Network Technologies (CNT), Sivakasi, India, pp. 314-318. https://doi.org/10.1109/CNT.2014.7062776
Abdullah, R. Y., Mary Posonia, A., & Barakkath Nisha, U. (2021). An adaptive mountain clustering based anomaly detection for distributed wireless sensor networks. 2021 International Conference on Communication, Control and Information Sciences (ICCISc), 1-6. https://doi.org/10.1109/ICCISc52257.2021.9484916
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