Abstract
Biometric authentication systems are increasingly being used to secure sensitive information and facilitate user identification in various applications. This article explores the role of neural networks in advancing biometric authentication systems, with a focus on techniques such as face recognition, fingerprint analysis, and voice recognition. It examines how neural networks, especially deep learning models, can enhance the accuracy, security, and scalability of biometric systems. Additionally, the article discusses the challenges and ethical considerations involved in implementing neural network-based biometric authentication, including issues related to data privacy, bias, and system robustness.
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