Hive tool instead of customary Etl in big data
Abstract :
Fingerprint Classification provides an important indexing mechanism in a fingerprint database. An accurate and consistent classification can greatly reduce fingerprint matching time for a large database. We present a fingerprint classification algorithm which is able to achieve accuracy better than previously reported in the literature. We classify fingerprints into seven categories: plain arch, tented arch, left loop, right loop, plain whorl, central pocket whorl and double loop whorl. The least square orientation estimation algorithm uses a novel representation to make a classification It has been tested on 4,000 images in the NIST-4 database. For the seven-class problem a classification accuracy of 95.08 percent is achieved. For the five-class problem (whorl, right loop, left loop, arch and tented arch) we are able to achieve a classification accuracy of 97 percent.
Keywords:
fingerprint classification,least square Orientation.
Citation: *,
( 2016), Hive tool instead of customary Etl in big data. Scientific Transactions in Environment and Technovation, 9(3): 144-148
Correspondence: S.Bhuvana