Fingerprint classification is a significant process by which identification procedure can be accelerated. Feature extraction might be afflicted with rotation. Thus, all images get through an introduced criterion to rectify rotated images. The core point of fingerprints is utilized widely in both classification and recognition process. In some cases, however, inaccurate location of it might contribute to incorrect categorization. Therefore, the common point is initiated for the purpose of better performance. Features are extracted according to the way ridges’ angles are distributed across images. Plus, kernel smoothing technique is used to enhance the process. Generalized regression neural network (GRNN) and Probabilistic neural network (PNN) are employed to classify fingerprints in four categories. Fingerprint verification competition (FVC) database is used to evaluate and train the networks. The simulation is performed by MATLAB and 97.4% accuracy is achieved for both GRNN and PNN.
| Published in | International Journal on Data Science and Technology (Volume 4, Issue 4) |
| DOI | 10.11648/j.ijdst.20180404.11 |
| Page(s) | 106-111 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Common Point, Fingerprint Classification, GRNN, Kernel, Neural Network, PNN, Rotation Rectification
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APA Style
Hemad Heidari Jobaneh. (2019). Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network. International Journal on Data Science and Technology, 4(4), 106-111. https://doi.org/10.11648/j.ijdst.20180404.11
ACS Style
Hemad Heidari Jobaneh. Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network. Int. J. Data Sci. Technol. 2019, 4(4), 106-111. doi: 10.11648/j.ijdst.20180404.11
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Download@article{10.11648/j.ijdst.20180404.11,
author = {Hemad Heidari Jobaneh},
title = {Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network},
journal = {International Journal on Data Science and Technology},
volume = {4},
number = {4},
pages = {106-111},
doi = {10.11648/j.ijdst.20180404.11},
url = {https://doi.org/10.11648/j.ijdst.20180404.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20180404.11},
abstract = {Fingerprint classification is a significant process by which identification procedure can be accelerated. Feature extraction might be afflicted with rotation. Thus, all images get through an introduced criterion to rectify rotated images. The core point of fingerprints is utilized widely in both classification and recognition process. In some cases, however, inaccurate location of it might contribute to incorrect categorization. Therefore, the common point is initiated for the purpose of better performance. Features are extracted according to the way ridges’ angles are distributed across images. Plus, kernel smoothing technique is used to enhance the process. Generalized regression neural network (GRNN) and Probabilistic neural network (PNN) are employed to classify fingerprints in four categories. Fingerprint verification competition (FVC) database is used to evaluate and train the networks. The simulation is performed by MATLAB and 97.4% accuracy is achieved for both GRNN and PNN.},
year = {2019}
}
TY - JOUR T1 - Fingerprint Classification Using Kernel Smoothing Technique and Generalized Regression Neural Network and Probabilistic Neural Network AU - Hemad Heidari Jobaneh Y1 - 2019/04/26 PY - 2019 N1 - https://doi.org/10.11648/j.ijdst.20180404.11 DO - 10.11648/j.ijdst.20180404.11 T2 - International Journal on Data Science and Technology JF - International Journal on Data Science and Technology JO - International Journal on Data Science and Technology SP - 106 EP - 111 PB - Science Publishing Group SN - 2472-2235 UR - https://doi.org/10.11648/j.ijdst.20180404.11 AB - Fingerprint classification is a significant process by which identification procedure can be accelerated. Feature extraction might be afflicted with rotation. Thus, all images get through an introduced criterion to rectify rotated images. The core point of fingerprints is utilized widely in both classification and recognition process. In some cases, however, inaccurate location of it might contribute to incorrect categorization. Therefore, the common point is initiated for the purpose of better performance. Features are extracted according to the way ridges’ angles are distributed across images. Plus, kernel smoothing technique is used to enhance the process. Generalized regression neural network (GRNN) and Probabilistic neural network (PNN) are employed to classify fingerprints in four categories. Fingerprint verification competition (FVC) database is used to evaluate and train the networks. The simulation is performed by MATLAB and 97.4% accuracy is achieved for both GRNN and PNN. VL - 4 IS - 4 ER -
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