Hybrid Approach for Face Recognition Using Convolutional Neural Networks, Fuzzy Logic, and SVM Classifier

Face recognition is a crucial area in computer vision and biometric applications, playing a significant role in security, authentication, and human-computer interaction. Despite its importance, traditional face recognition methods often encounter challenges such as variations in lighting, changes in facial expressions, and the presence of occlusions, which can affect their effectiveness. To address these limitations, hybrid approaches that combine multiple techniques have proven to enhance recognition accuracy and robustness, offering improved performance in diverse and challenging scenarios.This paper presents a hybrid approach combining Convolutional Neural Networks (CNN), Fuzzy Logic, and Support Vector Machine (SVM) classifiers for accurate and robust face recognition. The proposed model leverages the feature extraction capabilities of CNNs, the classification precision of SVMs, and the decision-making strength of Fuzzy Logic. Specifically, we use the ResNet50 CNN model to identify key facial features. The proposed method also employs Support Vector Machines (SVM) to achieve precise classification and fuzzy logic rules to refine the decision-making process by handling uncertainties and imprecise data effectively, enhancing the overall reliability of the system.Experimental results demonstrate the superiority of this hybrid model in terms of accuracy, efficiency, and robustness compared to existing techniques. Additionally, the results confirm that this hybrid approach enhances the robustness and adaptability of face recognition systems in real-world conditions. These findings highlight the potential of this integration to develop more reliable and efficient frameworks for practical applications.