Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping

Hemad Heidari Jobaneh

doi:doi:10.11648/j.mlr.20190401.12

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Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping

Hemad Heidari Jobaneh

Published in Machine Learning Research (Volume 4, Issue 1)

Received: 4 April 2019 Accepted: 16 May 2019 Published: 4 June 2019

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Abstract

The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.

Published in	Machine Learning Research (Volume 4, Issue 1)
DOI	10.11648/j.mlr.20190401.12
Page(s)	7-12
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), 2019. Published by Science Publishing Group

Keywords

ARTMAP, Fingerprint Recognition, GRNN, Kernel, Markov Chain, Neural Network

References

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[7]	M. Liu. “Fingerprint Classification Based on Singularities,” Pattern Recognition, Nov. 2009, pp. 1-5. doi: 10.1109/CCPR.2009.5343966.
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[9]	S. C. Dass, A. K. Jain. “Fingerprint Classification Using Orientation Field Flow Curves,” Proc. Proceedings of the Fourth Indian Conference on Computer Vision, Graphics & Image Processing, Dec. 2004, pp. 650-655.
[10]	S. M. Mohamed and H. O. Nyongesa, “Automatic Fingerprint Classification System Using Fuzzy Neural Techniques”, proc. Of the IEEE International Conference on Fuzzy System, 2002, Vol. 1, pp. 358-362.
[11]	K. Nandakumar, A. K. Jain, and S. Pankanti, “Fingerprint-based Fuzzy Vault: Implementation and Performance,” IEEE Trans. on Info. Forensics and Security, vol. 2, no. 4, pp. 744–757, December 2007.
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[14]	V. Conti, C. Militello, S. Vitabile and F. Sorbello, “An Embedded Fingerprints Classification System based on Weightless Neural Networks”, Frontiers in Artificial Intelligence and Applications – IOS Press Editor, Volume 193: New Directions in Neural Networks, 2009, pp. 67-75, ISSN 0922-6389, doi: 10.3233/978-1-58603-984-4-67.
[15]	A. Senior, “A Combination Fingerprint Classifier”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001, Vol. 23, No. 10, pp. 1165-1174.
[16]	Bhanu, B., Tan, X.: Fingerprint indexing based on novel features of minutiae triplets. IEEE Trans. Pattern Anal. Mach. Intell. 25 (5), 616–622 (2003).
[17]	H. Fronthaler, K. Kollreider, and J. Bigun. Local features for enhancement and minutiae extraction in fingerprints. Image Processing, IEEE Trans. on, 17 (3): 354 –363, march 2008.
[18]	Sankaran A, Pandey P, Vatsa M, et al. On latent fingerprint minutiae extraction using stacked denoising sparse auto encoders [C]// Biometrics (IJCB), 2014 IEEE International Joint Conference on. IEEE, 2014: 1-7.
[19]	R. Thai. (2003). Fingerprint Image Enhancement and Minutiae Extraction. The University of Western Australia. Retrieved from http://www.peterkovesi.com/studentprojects/raymondthai/RaymondThai.pdf.
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[23]	FVC2004 Fingerprint Verification Competition http://bias.csr.unibo.it/fvc2004 download.asp.

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APA Style

Hemad Heidari Jobaneh. (2019). Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Machine Learning Research, 4(1), 7-12. https://doi.org/10.11648/j.mlr.20190401.12

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ACS Style

Hemad Heidari Jobaneh. Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Mach. Learn. Res. 2019, 4(1), 7-12. doi: 10.11648/j.mlr.20190401.12

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AMA Style

Hemad Heidari Jobaneh. Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Mach Learn Res. 2019;4(1):7-12. doi: 10.11648/j.mlr.20190401.12

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@article{10.11648/j.mlr.20190401.12,
  author = {Hemad Heidari Jobaneh},
  title = {Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping},
  journal = {Machine Learning Research},
  volume = {4},
  number = {1},
  pages = {7-12},
  doi = {10.11648/j.mlr.20190401.12},
  url = {https://doi.org/10.11648/j.mlr.20190401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190401.12},
  abstract = {The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.},
 year = {2019}
}

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TY - JOUR
T1 - Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping
AU - Hemad Heidari Jobaneh
Y1 - 2019/06/04
PY - 2019
N1 - https://doi.org/10.11648/j.mlr.20190401.12
DO - 10.11648/j.mlr.20190401.12
T2 - Machine Learning Research
JF - Machine Learning Research
JO - Machine Learning Research
SP - 7
EP - 12
PB - Science Publishing Group
SN - 2637-5680
UR - https://doi.org/10.11648/j.mlr.20190401.12
AB - The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.
VL - 4
IS - 1
ER -

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Author Information

Hemad Heidari Jobaneh

Department of Electrical Engineering, Azad University, South Tehran Branch, Tehran, Iran

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APA Style

Hemad Heidari Jobaneh. (2019). Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Machine Learning Research, 4(1), 7-12. https://doi.org/10.11648/j.mlr.20190401.12

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ACS Style

Hemad Heidari Jobaneh. Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Mach. Learn. Res. 2019, 4(1), 7-12. doi: 10.11648/j.mlr.20190401.12

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AMA Style

Hemad Heidari Jobaneh. Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping. Mach Learn Res. 2019;4(1):7-12. doi: 10.11648/j.mlr.20190401.12

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@article{10.11648/j.mlr.20190401.12,
  author = {Hemad Heidari Jobaneh},
  title = {Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping},
  journal = {Machine Learning Research},
  volume = {4},
  number = {1},
  pages = {7-12},
  doi = {10.11648/j.mlr.20190401.12},
  url = {https://doi.org/10.11648/j.mlr.20190401.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20190401.12},
  abstract = {The necessity of fast and precise identification from fingerprints might be fulfilled via systems benefiting from intelligent elements such as Neural Networks. The process of recognition and classification have been performed according to beneficial points called core point, singularities, or minutiae. However, points always are sensitive to noise and distortion, thus inaccurate results. Hence, instead of extracting a point, two lines are defined to bring down the risk of finding a point. Plus, two approaches are proposed with the intention of extracting statistical features predicated upon Kernel and Markov chain. In fact, two sets of features are extracted from both horizontal and vertical Markov chain, derived from the ridges angle around the aforementioned lines. In addition, all features are trained and tested via two divergent neural networks, consisting Generalized Regression Neural Network (GRNN) and Adaptive Resonance Theory with mapping (ARTMAP). Fingerprint verification competition (FVC) database is used to analyze the system. The performances of networks with different sets of features are simulated and compared with MATLAB. The results coming from simulation are compared and 93.5% and 83.5% accuracy is achieved for GRNN and ARTMAP respectively. Furthermore, the system is tested by both networks with features coming from just vertical and horizontal features.},
 year = {2019}
}

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