In this code, we classify fingerprint by graph matching. In this method, at first input image is passed through preprocessing, and then four directional images are obtained. Then all directional images are divided into four section. A graph is produced based on the feature of each section. This graph is compared with the reference graph. The closest graph is considered as the final class.
Simulation results show 92.76 percent accuracy.
This code is with the database of images.
A.K. Jain, S. Prabhakar, L. Hong, A multichannel approach to fingerprint classification, IEEE Trans. Pattern Anal. Mach. Intell. 21 (4) (1999) 348-359.
A.M. Bazen, S.H. Gerez, Systematic methods for the computation of the directional fields and singular points of fingerprints, IEEE Trans. Pattern Anal. Mach. Intell. 24 (7) (2002) 905–919.
A.R. Rao, A Taxonomy for Texture Description and Identification, Springer Verlag, 1990.
A.W. Senior, A combination fingerprint classifier, IEEE Trans. Pattern Anal. Mach. Intell. 23 (10) (2001) 1165–1174.
A.W. Senior, A hidden Markov model fingerprint classifier, in: Proceedings of the 31st Asilomar Conference on Signals, Systems & Computers, Pacific Grove,CA, November 2–5, 1997.
B.M. Mehtre, N.N. Murthy, S. Kapoor, B. Chatterjee, Segmentation of fingerprint images using the directional image, Pattern Recogn. 20 (4) (1987) 429–435.
Bazen A, Gerez S (2002) Systematic methods for the computation of the direction fields and singular points of fingerprints. IEEE Trans Patt Anal Mach Intell 24(7):905–919.
Berry J, Stoney, “The history and development of fingerprinting” ،CRC Press, pp. 1–40,Florida, 2001 .
C. Jin, P. Jin, Fingerprint classification in DCT domain using RBF neural networks, J. Inform. Sci. Eng. 25 (6) (2009) 1955–1962.
C.I. Watson, M.D. Garris, E. Tabassi, C.L. Wilson, R.M. McCabe, S. Janet, K. Ko, User’s Guide to NIST Biometric Image Software (NBIS), National Institute of Standards and Technology.
C.L. Wilson, Massively parallel neural network fingerprint classification system, in: International Joint Conference on Neural Networks (IJCNN’92), Baltimore, MD, June 7–1, 1992.
Candela G, Grother P, Watson C, Wilkinson R, Wilson C (1995) PCASYS—a pattern-level classification automation system for fingerprints. National Institute of Standards and Technology; NISTIR 5647
Chong M, Ngee T, Jun L, Gay R (1997) Geometric framework for fingerprint image classification. Pattern Recognition 30(9):1475–1488.
D. Maltoni, D. Maio, A. Jain, S. Prabhakar, Handbook of Fingerprint Recognition, Springer-Verlag Inc., New York, 2009.
D. Peralta, M. Galar, I. Triguero, O. Miguel-Hurtado, J.M. Benitez, F. Herrera, Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms, Eng. Appl. Artif. Intell. 32 (2014) 37–53.
Dorasamy, Kribashnee, et al. “Fingerprint classification using a simplified rule-set based on directional patterns and singularity features.” Biometrics (ICB), 2015 International Conference on. IEEE, 2015.
 Ray, Santanu Saha “Graph Theory with Algorithms and its Applications” Springer, 2013
G.T. Candela, P.J. Grother, C.I. Watson, R.A. Wilkinson, C.L. Wilson, PCASYS – A Pattern-level Classification Automation System for Fingerprints, Tech. Rep., UNIST Interagency/Internal Report (NISTIR) – 5647, 1995.
H.-W. Jung, J.-H. Lee, Noisy and incomplete fingerprint classification using local ridge distribution models, Pattern Recogn. 48 (2) (2015) 473–484
Henry E (1900) Classification and uses of finger prints. Routledge, London.
J. Bigün, Pattern recognition in images by symmetries and coordinate transformations, Comput. Vis. Image Underst. 68 (3) (1997) 290–307.
J. Feng, A.K. Jain, Fingerprint reconstruction: from minutiae to phase, IEEE Trans. Pattern Anal. Mach. Intell. 33 (2) (2011) 209–223.
J. Hu, M. Xie, Fingerprint classification based on genetic programming, in: International Conference on Computer Engineering and Technology (ICCET), Chengdu, China, April 16–18, 2010.
J. Li, W.-Y. Yau, H. Wang, Combining singular points and orientation image information for fingerprint classification, Pattern Recogn. 41 (1) (2008) 353–366.
J. Luo, D. Song, C. Xiu, S. Geng, T. Dong, Fingerprint classification combining Curvelet transform and gray-level co-occurrence matrix, Math. Prob. Eng. 2014 (2014) 15 (Article ID 592928).
J.-H. Hong, J.-K. Min, U.-K. Cho, S.-B. Cho, C.H. Leung, Fingerprint classification using one-vs-all support vector machines dynamically ordered with Naïve Bayes classifiers, Pattern Recogn. 41 (2008) 662–671.
J.H. Wegstein, An Automated Fingerprint Identification System, NBS Special Publication 500-89.
J.-K. Min, J.-H. Hong, S.-B. Cho, Effective fingerprint classification by localized models of support vector machines, in: Proceedings of the International Conference on Advances in Biometrics (ICB’06), Hong Kong, China, January 5–7, 2006.
J.-K. Min, J.-H. Hong, S.-B. Cho, Fingerprint classification based on subclass analysis using multiple templates of support vector machines, Intell. Data Anal. 14 (3) (2010) 369–384.
Jain A, Minut S (2002) Hierarchical kernel fitting for fingerprint classification and alignment. Proc ICPR 2:469–473.
Jain A, Prabhakar S, Hong L (1999) A multichannel approach to fingerprint classification. IEEE Trans Patt Anal Mach Intell 21(4):348–359.
K. Cao, L. Pang, J. Liang, J. Tian, Fingerprint classification by a hierarchical classifier, Pattern Recognition. 46 (12) (2013) 3186–3197.
K. Karu, A.K. Jain, Fingerprint classification, Pattern Recogn. 29 (3) (1996)389–404.
K. Leung, C.H. Leung, Improvement of fingerprint retrieval by a statistical classifier, IEEE Trans. Inform. Forensics Secur. 6 (1) (2011) 59–69.
K. Nilsson, J. Bigun, Localization of corresponding points in fingerprints by complex filtering, Pattern Recogn. Lett. 24 (13) (2003) 2135–2144.
Kass M, Witkin A (1987) Analyzing oriented patterns. Comp Vis Graph Imag Proc 37(3):362–385.
M. Kass, A. Witkin, Analyzing oriented patterns, Comput. Vis. Graph. Image Process. 37 (3) (1987) 362–385.
M. Kawagoe, A. Tojo, Fingerprint pattern classification, Pattern Recogn. 17 (1984) 295–303.
M. Liu, Fingerprint classification based on adaboost learning from singularity features, Pattern Recogn. 43 (2010) 1062–1070.
Maio D, “Direct gray-scale minutiae detection in fingerprints” IEEE Trans. Pattern Anal, Machine Intell, vol 19 No1, pp 27–40, 1997.
Q. Zhang, H. Yan, Fingerprint classification based on extraction and analysis of singularities and pseudo ridges, Pattern Recogn. 37 (11) (2004) 2233–2243.
R. Balakrishnan, K. Ranganathan, “A Textbook of Graph Theory”, 2ed, Springer, 2012.
R. Cappelli, M. Ferrara, D. Maltoni, Minutia cylinder-code: a new representation and matching technique for fingerprint recognition, IEEE Trans. Pattern Anal. Mach. Intell. 32 (12) (2010) 2128–2141.
R.M. Stock, C.W. Swonger, Development and Evaluation of a Reader of Fingerprint Minutiae, Tech. Rep., Cornell Aeronautical Laboratory, Technical Report CAL No. XM-2478-X-1, 1969.
Ross A, Jain A, Reisman J,” A hybrid fingerprint matcher” Pattern Recognition ,vol 36, pp 1661 – 1673,2003.
Sherlock B .G, Monro D.M, Millard K “Fingerprint enhancement by directional Fourier filtering”. IEE Proc. Vision Image Sign, Process.vol 141 ,No 2,pp 87–94,1994
[ 47 ]Shimon Even, Guy Even, “Graph Algorithms” 2ed. ,CUP,2012
T. Kristensen, J. Borthen, K. Fyllingsnes, Comparison of neural network based fingerprint classification techniques, in: International Joint Conference on Neural Networks (IJCNN’07), Orlando, FL, August 12–17, 2007.
T.H. Le, H.T. Van, Fingerprint reference point detection for image retrieval based on symmetry and variation, Pattern Recogn. 45 (9) (2012) 3360–3372.
U. Rajanna, A. Erol, G. Bebis, A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion, Pattern Anal. Appl. 13 (3) (2010) 263–272.
Vitello, G., Contia, V., Vitabileb, S., & Sorbello, F. (2015). “An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks”. In Advances in Neural Networks: Computational and Theoretical Issues (pp. 217-227). Springer International Publishing. Chicago
X. Chen, J. Tian, X. Yang, A new algorithm for distorted fingerprints matching based on normalized fuzzy similarity measure, IEEE Trans. Image Process. 15 (3) (2006) 767–776.
X. Jiang, W. Yau, Fingerprint minutiae matching based on the local and global structures, Proceedings of the 15th International Conference on Pattern Recognition, vol. 2, IEEE, 2000.