Cart (Loading....) | Create Account
Close category search window
 

Efficient Finger Print Image Classification and Recognition using Neural Network Data Mining

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Umamaheswari, K. ; PSG Coll. of Technol., Coimbatore ; Sumathi, S. ; Sivanandam, S.N. ; Anburajan, K.K.N.

This paper deals with the fingerprint classification and recognition system, which consists of extracting and matching of minutiae from the input image. The necessity for security in fields such as improving airport security, strengthening the national borders, in travel documents, in preventing ID theft has brought the need to develop an able and efficient method for correct classification of personnel authentication. Fingerprints are the first biometric science used widely for the validation and verifications of entry into specific task, which is more reliable, efficient and accurate. Despite finger print recognition being reliable it has disadvantages such as very low recognition rate, low accuracy rate, total time of recovery and data insufficiency. To address above problem a novel data mining technique, neuro nearest neighbour based fingerprint classification and recognition, is introduced which boosts the classification rate. The proposed method consists of different stages such as image enhancement, line detector based feature extraction, neural network classification using Learning vector quantization and Back propagation networks. The proposed system is trained and tested on Fingerprint Database obtained from the University of Bologna Italy, which consists of 900 samples. The exact image is recognized precisely from the classified database rather than the original set of database using Crisp K-nearest neighbour algorithm that increases recognition accuracy and reduction in time

Published in:

Signal Processing, Communications and Networking, 2007. ICSCN '07. International Conference on

Date of Conference:

22-24 Feb. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.