Fuzzy Enhanced AI Facial Recognition System for Robust and Efficient Identification - S, Muthumarilakshmi; Balakrishnan, Biju; Narayanamoorthi, Thilagavathi; (Velammal), Revathi; R, Amudha (Journal of Neonatal Surgery, 2025)
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.