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Open Access Journal Article

Deep Learning Models for Biometric Authentication Systems

by Olivia Thomas 1,*
1
Olivia Thomas
*
Author to whom correspondence should be addressed.
TASC  2021, 16; 3(1), 16; https://doi.org/10.69610/j.tasc.20210217
Received: 7 January 2021 / Accepted: 20 January 2021 / Published Online: 17 February 2021
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Abstract

The rapid advancement in artificial intelligence and deep learning techniques has revolutionized the field of biometric authentication systems. This paper explores the integration of deep learning models in various biometric authentication methods, including facial recognition, fingerprint scanning, and iris recognition. The aim is to enhance the accuracy, speed, and robustness of these systems, while maintaining a high level of security. This study reviews the latest advancements in deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs) that are employed in biometric authentication. We discuss the challenges encountered in training these models, such as data imbalance, privacy concerns, and computational complexity. Furthermore, we analyze the effectiveness of deep learning algorithms in improving the authentication performance of current biometric systems, compare them with traditional methods, and highlight their potential for real-world applications. The paper concludes with a discussion on the future directions of research in this area, emphasizing the need for scalable and privacy-preserving solutions.

Keywords: Deep Learning Models, Biometric Authentication, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).;
Copyright: © 2021 by Thomas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Thomas, O. Deep Learning Models for Biometric Authentication Systems. Transactions on Applied Soft Computing, 2021, 3, 16. https://doi.org/10.69610/j.tasc.20210217
AMA Style
Thomas O. Deep Learning Models for Biometric Authentication Systems. Transactions on Applied Soft Computing; 2021, 3(1):16. https://doi.org/10.69610/j.tasc.20210217
Chicago/Turabian Style
Thomas, Olivia 2021. "Deep Learning Models for Biometric Authentication Systems" Transactions on Applied Soft Computing 3, no.1:16. https://doi.org/10.69610/j.tasc.20210217
APA style
Thomas, O. (2021). Deep Learning Models for Biometric Authentication Systems. Transactions on Applied Soft Computing, 3(1), 16. https://doi.org/10.69610/j.tasc.20210217

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