Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping
Machine Learning Research
Volume 4, Issue 1, March 2019, Pages: 7-12
Received: Apr. 4, 2019;
Accepted: May 16, 2019;
Published: Jun. 4, 2019
Views 293 Downloads 21
Hemad Heidari Jobaneh, Department of Electrical Engineering, Azad University, South Tehran Branch, Tehran, Iran
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.
Hemad Heidari Jobaneh,
Fingerprint Recognition Using Markov Chain and Kernel Smoothing Technique with Generalized Regression Neural Network and Adaptive Resonance Theory with Mapping, Machine Learning Research.
Vol. 4, No. 1,
2019, pp. 7-12.
E. R. Henry. “Classification and Uses of Fingerprints,” HM Stationery Office, 1905.
C. J. Lee, S. D. Wang, “Fingerprint feature reduction by principal Gabor basis function,” Pattern Recognition, 34, 2001, pp. 2245-2248.
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.
B. Bhanu and X. Tan, Fingerprint indexing based on novel features of minutiae triplets, IEEE Trans. PAMI, May 2003.
L. Wei, "Fingerprint Classification using Singularities Detection", International Journal of Mathematics and computers in simulation, Vol. 2, No. 2, pp. 158-162, 2008.
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.
M. Liu. “Fingerprint Classification Based on Singularities,” Pattern Recognition, Nov. 2009, pp. 1-5. doi: 10.1109/CCPR.2009.5343966.
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.
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.
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.
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.
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.
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.
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.
A. Senior, “A Combination Fingerprint Classifier”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 2001, Vol. 23, No. 10, pp. 1165-1174.
Bhanu, B., Tan, X.: Fingerprint indexing based on novel features of minutiae triplets. IEEE Trans. Pattern Anal. Mach. Intell. 25 (5), 616–622 (2003).
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.
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.
R. Thai. (2003). Fingerprint Image Enhancement and Minutiae Extraction. The University of Western Australia. Retrieved from http://www.peterkovesi.com/studentprojects/raymondthai/RaymondThai.pdf.
Fort, A., Mugnaini, M. and Vignoli, V. Hidden Markov Models Approach used for Life Parameters Estimations. Reliability Engineering and System Safety, vol. 136, pp. 85-91. 2015.
Gail A. Carpenter& Stephen Grossberg, "ADAPTIVE RESONANCE THEORY", 1998.
A. J. Al-Mahasneh, S. G. Anavatti, and M. A. Garratt, “Altitude identification and intelligent control of a flapping wing micro aerial vehicle using modified generalized regression neural networks,” in 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2017, pp. 2302–2307.
FVC2004 Fingerprint Verification Competition http://bias.csr.unibo.it/fvc2004 download.asp.