Genetic Algorithm and Probabilistic Neural Networks for Fingerprint Identification
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10.5120/17733-8846 |
Dhia Alzubaydi and Thikra Mohammed Abed. Article: Genetic Algorithm and Probabilistic Neural Networks for Fingerprint Identification. International Journal of Computer Applications 101(11):34-39, September 2014. Full text available. BibTeX
@article{key:article,
author = {Dhia Alzubaydi and Thikra Mohammed Abed},
title = {Article: Genetic Algorithm and Probabilistic Neural Networks for Fingerprint Identification},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {101},
number = {11},
pages = {34-39},
month = {September},
note = {Full text available}
}
Abstract
Existing security methods rely on knowledge based on approaches like password or token based on approaches like access cards. Such method is not very secure, biometrics such as fingerprint, face and voice offer means of personal identification and provide increased security because they rely on who we are. In this paper, algorithm fingerprint identification is introduced. The proposed algorithm has used 196 fingerprint image back to the twenty-eight individual 140 from them has been used for training and 56 image has been used for testing . Discrete Cosine Transform has been used to extract distinctive features from fingerprint image and genetic algorithm has been used as feature selection technique . Genetic algorithm has helped to produce GA filter in order to select subset of features out of DCT. When testing the proposed system by using Probabilistic Neural Network has found the identification rate reaching to 91%. This rate has emboldened on attempted using more one filter of genetic algorithm , the result that reached to 98% as identification rate with more reduction in number features.
References
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