Integrated IoT and Cloud Environment for Fingerprint Recognition

TL;DR

This paper presents a scalable cloud-based system for large-scale fingerprint matching that significantly reduces matching time using Aneka and Microsoft Azure resources.

Contribution

It introduces a novel cloud computing framework for large-scale fingerprint recognition leveraging Aneka and Azure, enhancing performance and scalability.

Findings

Matching time is substantially reduced.

System demonstrates scalability on cloud resources.

Effective for large biometric databases.

Abstract

Big data applications involving the analysis of large datasets becomes a critical part of many emerging paradigms such as smart cities, social networks and modern security systems. Cloud computing has developed as a mainstream for hosting big data applications by its ability to provide the illusion of infinite resources. However, harnessing cloud resources for large-scale big data computation is application specific to a large extent. In this paper, we propose a system for large-scale fingerprint matching application using Aneka, a platform or developing scalable applications on the Cloud. We present the design and implementation of our proposed system and conduct experiments to evaluate its performance using resources from Microsoft Azure. Experimental results demonstrate that matching time for biometric information such as fingerprints in large-scale databases can be reduced substantially using our proposed system.