Abstract
The development of face recognition technologies has become increasingly critical due to the growing need for effective identification methods. Traditional techniques often struggle with variations in illumination, pose, and facial expressions, limiting their applicability in real-world scenarios. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs), have significantly improved performance on benchmark datasets. Siamese Neural Networks, a specialized class of CNNs, have emerged as a highly promising solution for face recognition, offering unparalleled capabilities in learning feature representations and similarity metrics. This study rigorously examines the efficiency of Siamese Neural Networks in face recognition across diverse datasets and real-time scenarios. Using three distinct face recognition datasets, the research evaluates the accuracy and robustness of the network under challenging conditions and assesses its ability to distinguish between similar and dissimilar faces in real-time applications. The results demonstrate the effectiveness of Siamese Neural Networks in handling variations in pose, illumination, and expressions, highlighting their potential to advance face recognition technology. These findings provide valuable insights into the practical applicability of Siamese Neural Networks in real-world contexts.