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CLC number: TP316.4

On-line Access: 2016-08-05

Received: 2015-12-29

Revision Accepted: 2016-04-12

Crosschecked: 2016-07-11

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Dong-sheng LI


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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.8 P.766-780


Pegasus: a distributed and load-balancing fingerprint identification system

Author(s):  Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu

Affiliation(s):  National Laboratory for Parallel and Distributed Processing, College of Computer, National University of Defense Technology, Changsha 410003, China

Corresponding email(s):   zhaoyx1993@163.com, camu7s@163.com, dsli@nudt.edu.cn, maths_www@163.com, litoeknee@gmail.com

Key Words:  Distributed fingerprint identification, Distributed MongoDB, Load balancing

Yun-xiang Zhao, Wan-xin Zhang, Dong-sheng LI, Zhen Huang, Min-ne Li, Xi-cheng Lu. Pegasus: a distributed and load-balancing fingerprint identification system[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(8): 766-780.

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publisher="Zhejiang University Press & Springer",

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%A Yun-xiang Zhao
%A Wan-xin Zhang
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T1 - Pegasus: a distributed and load-balancing fingerprint identification system
A1 - Yun-xiang Zhao
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A1 - Min-ne Li
A1 - Xi-cheng Lu
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DOI - 10.1631/FITEE.1500487

Fingerprint has been widely used in a variety of biometric identification systems in the past several years due to its uniqueness and immutability. With the rapid development of fingerprint identification techniques, many fingerprint identification systems are in urgent need to deal with large-scale fingerprint storage and high concurrent recognition queries, which bring huge challenges to the system. In this circumstance, we design and implement a distributed and load-balancing fingerprint identification system named Pegasus, which includes a distributed feature extraction subsystem and a distributed feature storage subsystem. The feature extraction procedure combines the Hadoop Image Processing Interface (HIPI) library to enhance its overall processing speed; the feature storage subsystem optimizes MongoDB’s default load balance strategy to improve the efficiency and robustness of Pegasus. Experiments and simulations are carried out, and results show that Pegasus can reduce the time cost by 70% during the feature extraction procedure. Pegasus also balances the difference of access load among front-end mongos nodes to less than 5%. Additionally, Pegasus reduces over 40% of data migration among back-end data shards to obtain a more reasonable data distribution based on the operation load (insertion, deletion, update, and query) of each shard.

The authors present a distributed load balanced fingerprint identification system using big data technologies. The paper is interesting and novel.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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