Fingerprint Recognition Using Artificial Neural Networks

Fingerprint recognition has emerged as a crucial biometric authentication technology with diverse applications, such as access control, identity verification, and forensic investigations. This paper presents a comprehensive study on the application of Artificial Neural Networks (ANNs) in fingerprint recognition. ANNs, a subset of machine learning, have demonstrated remarkable potential in extracting distinctive features from fingerprint images and achieving high accuracy in fingerprint identification and verification tasks. In this paper, we delve into the theoretical foundations of ANNs, discuss their relevance in fingerprint recognition, and present an in-depth analysis of recent advancements, challenges, and prospects. Additionally, we provide insights into the key components of ANNs employed in fingerprint recognition, including data preprocessing, feature extraction, and classification, along with a review of prominent fingerprint datasets and evaluation metrics. This paper seeks to make a valuable addition to the existing knowledge in the field of fingerprint identification and stimulate additional research into improving the skills of artificial neural networks (ANNs) for biometric authentication.