Developing and Applying a Hybrid Machine Learning Model for Sturdy Face Recognition

Abstract: Face recognition plays a vital role in a wide range of security and personal identification applications—from surveillance systems to biometric authentication. Although recent advancements have greatly improved performance, current face recognition models still struggle with real-world challenges like variations in lighting, facial pose, age, and occlusion. In this paper, we present a hybrid machine learning approach that combines Convolutional Neural Networks (CNNs) for powerful feature extraction with Support Vector Machines (SVMs) for reliable classification. This combination is designed to improve both the accuracy and robustness of face recognition systems in diverse, real-world settings. We tested our model on well-known datasets such as Labeled Faces in the Wild (LFW) and VGGFace2, and the results show that our hybrid method consistently outperforms traditional models in terms of accuracy, precision, recall, and resilience to changes in facial features and conditions. These findings suggest that the proposed system is highly suitable for demanding applications like surveillance, border security, and other biometric identification tasks