Biometrics Employing Neural Network
Sajjad Bhuiyan

TL;DR
This paper reviews how neural networks improve biometric identification accuracy and security, emphasizing their potential to address current limitations in biometric systems.
Contribution
It provides a comprehensive survey of biometric techniques utilizing neural networks, highlighting their advantages in accuracy and security enhancement.
Findings
Neural networks significantly improve biometric recognition accuracy.
Biometric systems with neural networks offer enhanced security.
Ongoing research aims to further refine neural network classifiers.
Abstract
Biometrics involves using unique human traits, both physical and behavioral, for the digital identification of individuals to provide access to systems, devices, or information. Within the field of computer science, it acts as a method for identifying and verifying individuals and controlling access. While the conventional method for personal authentication involves passwords, the vulnerability arises when passwords are compromised, allowing unauthorized access to sensitive actions. Biometric authentication presents a viable answer to this problem and is the most secure and user-friendly authentication method. Today, fingerprints, iris and retina patterns, facial recognition, hand shapes, palm prints, and voice recognition are frequently used forms of biometrics. Despite the diverse nature of these biometric identifiers, the core objective remains consistent ensuring security,…
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Taxonomy
TopicsCognitive Computing and Networks · Biometric Identification and Security · User Authentication and Security Systems
MethodsPathways Language Model
