DFRS: A Large-Scale Distributed Fingerprint Recognition System Based on Redis

Abstract

As the fast growth of users, matching a given fingerprint with the ones in a massive database precisely and efficiently becomes more and more difficult. To fight against this challenging issue in “big data” era, we have designed in this paper a novel large-scale distributed Redis-based fingerprint recognition system called DFRS that introduces an innovative framework for fingerprint processing while incorporating many key technologies for data compression and computing acceleration. By using Base64 compressive encoding method together with key-value pair storage structure, the space reduction can be achieved up to 40 % in our experiments – which is particularly important as Redis is an in memory read-write NoSQL data storage system. To compensate the cost introduced by compressive encoding, the parallel decoding is adopted with the help of OpenMP, saving the time by above one third. Furthermore, the granularity-based division (RM$+$AM architecture) and the Quick-Return strategy bring significant improvement in matching time, making the whole system – DFRS feasible and efficient in large scale for massive data volume.

Y. Peng—This work was supported by the National Basic Research Program of China (973) under Grant No. 2014CB340303, National Natural Science Foundation of China (NSF) under Grant No. 61402490, the Science and Technology Commission of Shanghai Municipathy under research Grant No. 14DZ2260800, China Postdoctoral Science Foundation under Grant No. 2014M561438 and Excellent Ph.D. Dissertation Foundation of Hunan.