Neural Network Based Recognition by Using Genetic Algorithm for Feature Selection of Enhanced Fingerprints

Authors:
Adem Alpaslan Altun;Novruz Allahverdi
Affiliations:
Selcuk University, Technical Education Faculty, Department of Computer Systems Education, 42031 Konya, Turkey;Selcuk University, Technical Education Faculty, Department of Computer Systems Education, 42031 Konya, Turkey
Venue:
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Year:
2007

Citing 5
Cited 1

Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Fingerprint Image Enhancement: Algorithm and Performance Evaluation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature Transformation and Subset Selection

IEEE Intelligent Systems
Fingerprint classification and matching using a filterbank

Fingerprint classification and matching using a filterbank
Filterbank-based fingerprint matching

IEEE Transactions on Image Processing

Soft computing decision support for a steel sheet incremental cold shaping process

IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning

Quantified Score

Hi-index	0.00

Visualization

Abstract

In order to ensure that the performance of a fingerprint recognition system will be powerful with respect to the quality of input fingerprint images, the enhancement of fingerprints is essential. In this study wavelet transform and contourlet transform which is a new extension of the wavelet transform in two dimensions are applied for fingerprint enhancement. In addition, feature selection is a process that chooses a subset of features from the original fingerprint features so that the feature space is optimally reduced according to a certain criterion. In this study, a Genetic Algorithms (GAs) approach to fingerprint feature selection is proposed and selected features are input to Artificial Neural Networks (ANNs) for fingerprint recognition. The performance has been tested on fingerprint recognition.