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), 2019. Published by Science Publishing Group |
Common Point, Fingerprint Classification, GRNN, Kernel, Neural Network, PNN, Rotation Rectification
| [1] | E. R. Henry. “Classification and Uses of Fingerprints,” HM Stationery Office, 1905. |
| [2] | C. J. Lee, S. D. Wang, “Fingerprint feature reduction by Principal Gabor basis function,” Pattern Recognition, 34, 2001, pp. 2245-2248. |
| [3] | C. Park, S. Oh, D. Kwak, B. Kim, Y Song, and K Park, “A new reference point detection algorithm based on orientation pattern labeling in fingerprint images”, pp. 697-703, Pattern Recognition and Image Analysis, First Iberian Conference, IbPRIA 2003. |
| [4] | B. Bhanu and X. Tan, Fingerprint indexing based on novel features of minutiae triplets, IEEE Trans. PAMI, May 2003. |
| [5] | L. Wei, "Fingerprint Classification using Singularities Detection", International Journal of Mathematics and computers in simulation, Vol. 2, No. 2, pp.158-162, 2008. |
| [6] | Iwasokun, Gabriel Babatunde, and O. C. Akinyokun. “Fingerprint Singular Point Detection Based on Modified Poincare Index Method.” International Journal of Signal Processing Image Processing & Pattern Recognition 7, 2014. |
| [7] | M. Liu. “Fingerprint Classification Based on Singularities,” Pattern Recognition, Nov. 2009, pp. 1-5. doi: 10.1109/CCPR.2009.5343966. |
| [8] | P. Gnanasivam and S. Muttan, “An efficient algorithm for fingerprint preprocessing and feature extraction”, ICEBT 2010, Procedia computer Science, Vol. 2, 2010, pp.133-142. |
| [9] | H. Jung, J. H. Lee. “Noisy and Incomplete Fingerprint Classification Using Local Ridge Distribution Models,” Pattern Recognition, vol. 48, Feb. 2015, pp. 473-484, doi: 10.1016/j.patcog.2014.07.030. |
| [10] | 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. |
| [11] | 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 |
| [12] | 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. |
| [13] | Surmacz, K., Saeed, K., Rapta, P., “An improved algorithm for feature extraction from a fingerprint fuzzy image”, Optica Applicata, Volume 43 – No. 3, 2013, Pages 515 – 527 |
| [14] | S. R. Patil and S. R. Suralkar, “Fingerprint Classification using Artificial Neural Network”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 10, pp. 513-517, 2012, ISSN 2250-2459. |
| [15] | 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, Volume193: New Directions in Neural Networks, 2009, pp. 67-75, ISSN 0922-6389, doi:10.3233/978-1-58603-984-4-67. |
| [16] | A. Senior, “A Combination Fingerprint Classifier”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001, Vol. 23, No. 10, pp. 1165-1174 |
| [17] | R, Thai., Fingerprint Image Enhancement and Minutiae Extraction. The University of Western Australia. Retrieved from, 2003. |
| [18] | Wasserman, P. D., Advanced Methods in Neural Computing, New York, Van Nostrand Reinhold, 1993, pp. 155–61. |
| [19] | Wasserman, P. D., Advanced Methods in Neural Computing, New York, Van Nostrand Reinhold, 1993, pp. 35–55. |
| [20] | Galar, M., et al. A survey of fingerprint classification Part II: Experimental analysis and ensemble proposal. Knowledge-Based Systems 81, 2015. |
| [21] | Daniel, Holden. Jun Saito, Taku Komura., A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics, 2016, Volume 35, Issue 4, Article 138. |
| [22] | C, Wu. S, Tulyakov., and V, Govindaraju., “Image quality measures for fingerprint image enhancement,” Multimedia Content Representation, Classification and Security, 2006, pp. 215–222. |
| [23] | A, Awasthi., K, Venkataramani., and A, Nandini., “Image quality quantification for fingerprints using quality-impairment assessment,” in Applications of Computer Vision (WACV), IEEE, 2013, pp. 296–302. |
| [24] | K, Cao. L, Pang. J, Liang. et al. “Fingerprint Classification by a Hierarchical Classifier,” Pattern Recognition, 2013, vol. 46, pp. 3186-3197, doi: 10.1016/j.patcog.2013.05.008. |
| [25] | J, Li., W, Yau., H, Wang., “Combining Singular Points and Orientation Image Information for Fingerprint Classification,” Pattern Recognition, 2008, vol. 41, pp. 353-366, doi: 10.1016/j.patcog.2007.03.015. |
| [26] | Yazdi M., and Gheysari K., “A New Approach for the Fingerprint Classification Based on Gray-Level Co-Occurrence Matrix," Proceedings Of World Academy Of Science, Engineering And Technology, vol. 30, July 2008. |
| [27] | Yao Y., et. al., “A new machine learning approach to fingerprint classification," 7th Congress of the Italian Association for Artificial Intelligence, pp. 57-63, 2001. |
| [28] | Y. Yao, G. L. Marcialis, M. Pontil, P. Frasconi and F. Roli, Combining flat and structured representations for fingerprint classification with recursive neural networks and support vector machines, Pattern Recognition, vol. 36, no. 2, pp. 397-406, Feb. 2003. |
| [29] | A. K. Jain and S. Minut, Hierarchical kernel fitting for fingerprint classification and alignment, Proc. ICPR, vol. 2, pp. 469-473, 2002. |
| [30] | FVC2004 Fingerprint Verification Competition http: //bias.csr.unibo.it/fvc2004 download.asp. |
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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