Fingerprint Identification using Discretization Technique
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32807
Fingerprint Identification using Discretization Technique

Authors: W. Y. Leng, S. M. Shamsuddin

Abstract:

Fingerprint based identification system; one of a well known biometric system in the area of pattern recognition and has always been under study through its important role in forensic science that could help government criminal justice community. In this paper, we proposed an identification framework of individuals by means of fingerprint. Different from the most conventional fingerprint identification frameworks the extracted Geometrical element features (GEFs) will go through a Discretization process. The intention of Discretization in this study is to attain individual unique features that could reflect the individual varianceness in order to discriminate one person from another. Previously, Discretization has been shown a particularly efficient identification on English handwriting with accuracy of 99.9% and on discrimination of twins- handwriting with accuracy of 98%. Due to its high discriminative power, this method is adopted into this framework as an independent based method to seek for the accuracy of fingerprint identification. Finally the experimental result shows that the accuracy rate of identification of the proposed system using Discretization is 100% for FVC2000, 93% for FVC2002 and 89.7% for FVC2004 which is much better than the conventional or the existing fingerprint identification system (72% for FVC2000, 26% for FVC2002 and 32.8% for FVC2004). The result indicates that Discretization approach manages to boost up the classification effectively, and therefore prove to be suitable for other biometric features besides handwriting and fingerprint.

Keywords: Discretization, fingerprint identification, geometrical features, pattern recognition

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1083113

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2308

References:


[1] N.K. Ratha, K. Karu, S. Chen, A.K. Jain. A Real-Time Matching System for Large Fingerprint Databases. IEEE Trans. Pattern Anal. Mach. Intell., 1996: 799-813.
[2] N. Nain, B.M. Deepak, D. Kumar, M. Baswal, and B. Gautham, Optimized Minutiae-Based Fingerprint Matching. Lecture Notes in Engineering and Computer Science, vol. 2170(1), pp. 682-687,2008.
[3] M.K. Umair, A.K. Shoab, N. Ejaz, and R. Riaz, "A Fingerprint Verification System using Minutiae and. Wavelet Based Features," International Conference on Emerging. Technologies, pp. 291-296, 2009.
[4] K. Abbad, N. Assem, H. Tairi and A. Aarab, "Fingerprint Matching Relying on Minutiae Hough Clusters", ICGST-International Journal on Graphics, Vision and Image Processing, vol. 10(1), 2010.
[5] B. Roli, S. Priti and B. Punam, "Effective Morphological Extraction of True Fingerprint Minutiae based on the Hit or Miss Transform" International Journal of Biometric and Bioinformatics, vol. 4(2), pp. 71- 85, 2010.
[6] Y. Yin, J.Tian and X.K Yang," Ridge Distance Estimation in Fingerprint Images: Algorithm and Performance Evaluation" EURASIP Journal on Applied Signal Processing, 2004.
[7] A K Jain and F Farrokhnia. "Unsupervised texture segmentation using Gabor filters". Pattern Recognition. vol. 12, 1991, pp.238-241.
[8] S Greenberg, M Aladjem, D Kogan and I. Dimitrov. "Fingerprint image enhancement using filtering techniques". Proceeding 15th Internat. Conference on Pattern Recognition III. Barcelona, Spain. 2000, pp.326- 329.
[9] B. G. Kim and D.J. Park, ÔÇÿÔÇÿAdaptive image normalization based on block processing for enhancement of fingerprint image", Electronics Letters, IEE, Volume 38, Isuue:14, p.p 696-698.
[10] A. Mishra and M. Shandilya, "Fingerprint-s Core Point Detection Using Gradient Field Mask" International Journal of Computssser Applications (0975 - 8887), Volume 2 - No.8, June 2010.
[11] A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proc. IEEE, vol. 85, pp. 1365-1388, Sept. 1997.
[12] E. C. Driscoll, C. O. Martin, K. Ruby, J. J. Russel, and J. G. Watson, "Method and apparatus for verifying identity using image correlation," U.S. Patent 5 067 162, 1991.
[13] A. Sibbald, "Method and apparatus for fingerprint characterization and recognition using auto-correlation pattern," U.S. Patent 5 633 947, 1994.
[14] D. K. Karna, S. Agarwal, and S. Nikam, "Normalized Cross-correlation based Fingerprint Matching," in Fifth International Conference on Computer Graphics, Imaging and Visualization, 2008, pp. 229 - 232.
[15] A. K. Jain, L. Hong, and R. Bolle, "On-line Fingerprint Verification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, pp. 302 - 314, April, 1997, 1997.
[16] A.K. Muda, S.M. Shamsuddin. and M. Darus, "Invariants Discretization for Individuality Representation in Handwritten Authorship," International Workshop on Computational Forensic (IWCF 2008), LNCS 5158, Springer Verlag, pp. 218- 228.
[17] B. O. Mohammed and S. M. Shamsuddin, Feature Discretization for Individuality Representation in Twins Handwritten Identification, Journal of Computer Science 7(7) (2011), pp. 1080-1087.
[18] W. D. Fisher, On grouping for maximum homogeneity, Journal of the American Statistical Association 53(284) (1958), pp. 789-798.
[19] J. Dougherty, R. Kohavi and M. Sahami, Supervised and unsupervised discretization of continuous features, in A. Prieditis & S. Russell (Eds.), in Int. Conf. on Machine Learning (San Francisco, 1995), pp. 194-202.
[20] M. C. Ludl and G. Widmer, Relative Unsupervised Discretization for Association Rule Mining, in European Conference on Principles of Data Mining and Knowledge Discovery (European, 2000), pp. 148-158.
[21] R. Ahmad, M. Darus, S. M. Shamsuddin and A. A. Bakar, Pendiskretan Set Kasar Menggunakan Ta-akulan Boolean Terhadap Pencaman Simbol Matematik, Information Technology & Multimedia (2004) 15-26.
[22] A. Kumar and D. Zhang, Hand geometry recognition using entropybased discretization, IEEE Transaction on Information Forensics and Security 2 (2) (2007), pp. 181-187.
[23] J. Zou and C. C. Liu, Discretized Gabor Statistical Models for Face Recognition, Digital Content Technology and its Applications (JDCTA) 5(5) (2011), pp. 175-181.
[24] K. Kianmehr, M. Alshalalfa and R. Alhajj, Fuzzy clustering-based discretization for gene expression classification, Knowledge and Information Systems (Published online, 2009).
[25] K. Sarojini, K. Thangavel and D. DXevakumari, Feature Subset Selection based on Modified Fuzzy Relative Information Measure for classifier, Engineering Science and Technology 2(5) (2010) 2456-2465.
[26] J. Gama and C. Pinto, Discretization from Data Streams: applications to Histograms and Data Mining, in ACM Symposium on Applied Computing, (ACM Press, New York, 2006), pp. 662-667.
[27] M.M. Min and Y. Thein, "Intelligent Fingerprint Recognition System by Using Geometry Approach" IEEE International Conference on Current Trends in Information Technology,pp.1-5,2009
[28] M. Poulos, E. Magkos, V. Chrissikopoulos, N. Alexandris, "Secure fingerprint verification based on image processing segmentation using computational geometry algorithms", 2003
[29] C. Lee and D. G. H. Shin, A context-sensitive discretization of numeric attributes for classification learning. In: Proceedings of the eleventh European conference on artificial intelligence. Amsterdam: Wiley; 1994.p. 428-32
[30] C.H. Wu, "Advance Feature Extraction Algorithms for Automatic Fingerprint Recognition Systems", Dissertation on The University of New York, April 2007.
[31] J. Komorowski, A. ├ÿhrn and A. Skowron (2002). The ROSETTA Rough Set Software System, In Handbook of Data Mining and Knowledge Discovery, W. Klösgen and J. Zytkow (eds.), ch. D.2.3, Oxford University Press. ISBN 0-19-511831-6.