Fingerprint classification with combined neural networks

Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability o...

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Bibliographic Details
Main Author:	Zhang, Fan
Published:	University of Surrey 2009
Subjects:	363.24
Online Access:	http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493220

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spelling	ndltd-bl.uk-oai-ethos.bl.uk-4932202015-03-20T04:09:57ZFingerprint classification with combined neural networksZhang, Fan2009Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.363.24University of Surreyhttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493220http://epubs.surrey.ac.uk/2216/Electronic Thesis or Dissertation
collection	NDLTD
sources	NDLTD
topic	363.24
spellingShingle	363.24 Zhang, Fan Fingerprint classification with combined neural networks
description	Biometric identification has been widely used in identifying a genuine person from an impostor. Fingerprint identification is becoming a very popular biometric identification technique because it has special properties: fingerprints are unique and unchangeable. With increased processing capability of computers and larger the size of fingerprint databases are increased, the demand for higher speed processing and greater processing capacity for automatic fingerprint identification systems (AFIS) has increased. APIS consists of fingerprint feature acquisition, fingerprint classification and fingerprint matching. Fingerprint classification plays a key role in fingerprint identification as efficient and accurate algorithms cannot only reduce the search time for searching large fingerprint databases, but they can also reduce the number of fingerprints that need to be searched.
author	Zhang, Fan
author_facet	Zhang, Fan
author_sort	Zhang, Fan
title	Fingerprint classification with combined neural networks
title_short	Fingerprint classification with combined neural networks
title_full	Fingerprint classification with combined neural networks
title_fullStr	Fingerprint classification with combined neural networks
title_full_unstemmed	Fingerprint classification with combined neural networks
title_sort	fingerprint classification with combined neural networks
publisher	University of Surrey
publishDate	2009
url	http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.493220
work_keys_str_mv	AT zhangfan fingerprintclassificationwithcombinedneuralnetworks
_version_	1716783994759020544

Fingerprint classification with combined neural networks

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