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Feature Level Fusion for Fingerprint using Neural Network for Person Identification

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IJCA Proceedings on International Conference on Cognitive Knowledge Engineering
© 2018 by IJCA Journal
ICKE 2016 - Number 1
Year of Publication: 2018
Authors:
Siddiqui Almas
Lothe Savita A.
Telgadrupali L.
Deshmukh P. D.

Siddiqui Almas, Lothe Savita A., Telgadrupali L. and Deshmukh P D.. Article: Feature Level Fusion for Fingerprint using Neural Network for Person Identification. IJCA Proceedings on International Conference on Cognitive Knowledge Engineering ICKE 2016(1):41-45, January 2018. Full text available. BibTeX

@article{key:article,
	author = {Siddiqui Almas and Lothe Savita A. and Telgadrupali L. and Deshmukh P. D.},
	title = {Article: Feature Level Fusion for Fingerprint using Neural Network for Person Identification},
	journal = {IJCA Proceedings on International Conference on Cognitive Knowledge Engineering},
	year = {2018},
	volume = {ICKE 2016},
	number = {1},
	pages = {41-45},
	month = {January},
	note = {Full text available}
}

Abstract

Security plays a very important role in one's life. Biometrics is an effective technology for personnel identity authentication. It has the capability to reliably distinguish between an authorized people. This paper presents the fusion of fingerprint modalities at Rank level fusion as well as feature level fusion. This paper includes well-known feature extraction method of Gabor Filter in rank level fusion and minutiae feature extraction method for feature level fusion. Decision making approach is used at rank level and Neural Network approach is used for matching at feature level fusion. Multiple instances for one biometric traits are used. The system activate through artificial neural network. The proposed approach for feature level fusion provides the better result. The recognition rate is increased & the error rate is decreased by with the help of this system.

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