Face recognition technology has witnessed remarkable advancements with the emergence of Convolutional Neural Networks (CNNs). This paper presents an in-depth review of CNN-based models for robust face recognition, exploring their architectural intricacies and practical implications. The proliferation of facial data and the need for accurate and efficient identification have propelled CNNs to the forefront of face recognition research. This review delves into the fundamental components of CNN architectures, including convolutional layers, pooling layers, and fully connected layers, highlighting their roles in feature extraction and classification. Moreover, the study elucidates various strategies employed to enhance CNN performance, such as transfer learning and data augmentation. By synthesizing findings from multiple studies, the review discusses benchmarks, datasets, and evaluation metrics used to assess the effectiveness of CNN-based face recognition models. The synthesis of research contributions and practical insights underscores the transformative potential of CNNs in achieving robust and accurate face recognition, thereby fostering advancements in security, surveillance, and biometric applications.

Face recognition technology has witnessed remarkable advancements with the emergence of Convolutional Neural Networks (CNNs).