A Complete Fingerprint Matching Algorithm on GPU for a Large Scale Identification System

Le, Hong Hai; Nguyen, Ngoc Hoa; Nguyen, T.

doi:10.1007/978-981-10-0557-2_67

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…Similarly, their framework is also flexible to any kind of fingerprint matching algorithms. Le et al [7] and Cappelli et al [8] benefit from graphics processing unit (GPU) computing implementation of a fingerprint matching algorithm to speed up their system performance on large databases. These algorithms have been implemented completely using CUDA [9] where they have used different parallelization approaches.…”

Section: Related Workmentioning

confidence: 99%

Integrated IoT and Cloud Environment for Fingerprint Recognition

Toosi,

Godaz

et al. 2018

Preprint

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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 for 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.

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Section: Related Workmentioning

confidence: 99%

Integrated IoT and Cloud Environment for Fingerprint Recognition

Toosi,

Godaz

et al. 2018

Preprint

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“…The main advantage is the linear scalability to any number of devices without introducing any data dependencies. Le et al proposed a minuta cylinder‐code 3D representation to provide both good accuracy and speedup, increasing the performance to 1.8 million matches per second on a single GPU. Cappelli et al implemented a multi‐GPU automated fingerprint identification system scaling the throughput to up to 35 million matches per second while keeping similar accuracy as compared with related state‐of‐the‐art systems.…”

Section: Applicationsmentioning

confidence: 99%

A survey on graphic processing unit computing for large‐scale data mining

Cano

2017

WIREs Data Min & Knowl

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General purpose computation using Graphic Processing Units (GPUs) is a wellestablished research area focusing on high-performance computing solutions for massively parallelizable and time-consuming problems. Classical methodologies in machine learning and data mining cannot handle processing of massive and high-speed volumes of information in the context of the big data era. GPUs have successfully improved the scalability of data mining algorithms to address significantly larger dataset sizes in many application areas. The popularization of distributed computing frameworks for big data mining opens up new opportunities for transformative solutions combining GPUs and distributed frameworks. This survey analyzes current trends in the use of GPU computing for large-scale data mining, discusses GPU architecture advantages for handling volume and velocity of data, identifies limitation factors hampering the scalability of the problems, and discusses open issues and future directions.

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A Complete Fingerprint Matching Algorithm on GPU for a Large Scale Identification System

Cited by 2 publications

References 21 publications

Integrated IoT and Cloud Environment for Fingerprint Recognition

Integrated IoT and Cloud Environment for Fingerprint Recognition

A survey on graphic processing unit computing for large‐scale data mining

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