DEEP NEURAL NETWORKS FOR BIOMETRIC AUTHENTICATION IN INFORMATION SECURITY
| dc.date.accessioned | 2025-02-10T15:16:34Z | |
| dc.date.available | 2025-02-10T15:16:34Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | In the rapidly evolving landscape of information security, biometric authentication has emerged as a critical mechanism for enhancing identity verification processes. Deep Neural Networks (DNNs), with their exceptional capability to learn complex patterns and representations from large datasets, have significantly advanced the accuracy and robustness of biometric systems. This paper explores the application of DNNs in biometric authentication, focusing on modalities such as fingerprint recognition, facial recognition, iris scanning, and voice authentication. We discuss the architectural innovations in deep learning, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), that contribute to improved feature extraction, noise reduction, and resistance to spoofing attacks. Furthermore, the paper addresses the challenges associated with deploying DNN-based biometric systems, such as data privacy concerns, model interpretability, and susceptibility to adversarial attacks. By analyzing recent advancements and experimental results, we demonstrate how DNNs enhance the security, scalability, and efficiency of biometric authentication in modern information security infrastructures. | |
| dc.identifier.uri | https://africarxiv.ubuntunet.net/handle/1/1824 | |
| dc.title | DEEP NEURAL NETWORKS FOR BIOMETRIC AUTHENTICATION IN INFORMATION SECURITY |
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