Name
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 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 upi¾?to 40i¾?% 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. |
Year | Venue | Field |
|---|---|---|
2016 | MMM | Data mining,Base64,Fingerprint recognition,Computer data storage,Computer science,NoSQL,Artificial intelligence,Distributed computing,Pattern recognition,Fingerprint,Data compression,Big data,Encoding (memory) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing Li | 1 | 217 | 60.28 |
| Zhen Huang | 2 | 57 | 20.78 |
| Jinbang Chen | 3 | 12 | 3.66 |
| Yifan Yuan | 4 | 0 | 1.01 |
| Yuxing Peng | 5 | 194 | 45.66 |