DFIS: A Scalable Distributed Fingerprint Identification System
Fingerprint has been widely used in a variety of biometric identification systems. However, with the rapid development of fingerprint identification systems, the amount of fingerprints information stored in systems has been rising sharply, making it challenging to process and store fingerprints efficiently and robustly with traditional stand-alone systems and relational databases. In this paper, we propose a scalable distributed fingerprint identification system, named DFIS. It combines the feature extraction procedure with HIPI library and optimizes the load balance strategy of MongoDB to construct a much more robust and stable system. Related experiments and simulations have been carried out and results show that DFIS can reduce the time expense by \(70\,\%\) during the features extraction procedural. For load balance of MongoDB, DFIS can decrease the difference of access load to less than \(5\,\%\) and meanwhile decrease \(50\,\%\) data migration to gain more reasonable distribution of operation load and data load among shards compared with the default load balance strategy in MongoDB.
KeywordsFingerprint identification Distributed computing HIPI MongoDB
This work is supported by the National Basic Research Program of China (973) under Grant No.2014CB340303, the National Natural Science Foundation of China under Grant No.61222205, No.61402490, and No.61303064. This work is also supported by the Program for New Century Excellent Talents in University, the Fok Ying-Tong Education Foundation under Grant No. 141066, and Foundation of Distinguished PHD Thesis of Hunan Province.
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