CFP last date
22 April 2024
Reseach Article

Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields

by Meghna B. Patel, Satyen M. Parikh, Ashok R. Patel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 167 - Number 2
Year of Publication: 2017
Authors: Meghna B. Patel, Satyen M. Parikh, Ashok R. Patel
10.5120/ijca2017914176

Meghna B. Patel, Satyen M. Parikh, Ashok R. Patel . Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields. International Journal of Computer Applications. 167, 2 ( Jun 2017), 12-18. DOI=10.5120/ijca2017914176

@article{ 10.5120/ijca2017914176,
author = { Meghna B. Patel, Satyen M. Parikh, Ashok R. Patel },
title = { Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 167 },
number = { 2 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 12-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume167/number2/27742-2017914176/ },
doi = { 10.5120/ijca2017914176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:13:44.511718+05:30
%A Meghna B. Patel
%A Satyen M. Parikh
%A Ashok R. Patel
%T Performance Improvement in Gradient based Algorithm for the Estimation of Fingerprint Orientation Fields
%J International Journal of Computer Applications
%@ 0975-8887
%V 167
%N 2
%P 12-18
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Accuracy of fingerprint recognition system is reliable for correct measurement of fingerprint features. Orientation estimation of fingerprint ridges is playing a vital role in image enhancement, segmentation, classification and recognition. The accurate estimation of ridge orientation improves the performance of minutiae extraction and matching algorithm. The noisy fingerprint does not contain the clear ridge structure, that’s why ridge orientation estimation is the toughest and challenging task in fingerprint image enhancement. Gradient-based orientation estimation algorithm is widely adopted and most popular method accepted in literature. This paper enhance the consistency level of ridge orientation after changing the range of output direction from [-PI/4, PI/4] to [0, PI] and remove the inconsistency. The implementation is done using java language and the experimental result is made on FVC2000 and FingerDOS databases. The outcome of enhanced new method for estimating ridge orientation give better performance than the existing gradient based approach.

References
  1. Biometrics Market And Industry Report 2009-2014. International Biometric Group, New York, 2009.
  2. Cappelli R., Maio D., Wayman J. L., Jain A. K., Performance Evaluation Of Fingerprint Verification Systems. IEEE Transactions On Pattern Analysis And Machine Intelligence, 2006, Vol. 28, No. 1, Pp. 3-18.
  3. Hong L., Jain A. K., Wan Y., Fingerprint Image Enhancement: Algorithm And Performance Evaluation. IEEE Trans. On Pattern Analysis And Machine Intelligence, 1998, Vol. 20, No. 8, Pp. 777-789.
  4. Chikkerur S., Cartwright A. N., Govindaraju V., Fingerprint Enhancement Using Stft Analysis. Pattern Recogn. 2007, Vol. 40, No. 1, Pp. 198-211.
  5. Yi Wang, Jiankun Hu, Damien Phillips, “A Fingerprint Orientation Model Based On 2d Fourier Expansion (Fomfe) And Its Application To Singular-Point Detection And Fingerprint Indexing”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 29, No. 4, Pp.573-585, 2007.
  6. CarstenGottschlich, PredaMihailesc, Axel Munk, “Robust Orientation Field Estimation And Extrapolation Using Semilocal Line Sensors”, IEEE Transactions On Image And Signal Processing And Analysis, Pp. 529-533. 2009.
  7. S. Yoon, J. Feng, A. Jain, J.2011. Latent Fingerprint Enhancement Via Robust Orientation Field Estimation. In Proceedings Of IJCB( International Joint Conference On Biometrics.
  8. Jianjiang Feng, Jie Zhou, Anil K. Jain, “Orientation Field Estimation For Latent Fingerprint Enhancement”, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 54, No. 4, Pp. 925–940, 2013.
  9. Prasad Reddy P.V.G.D, M. James Stephen, “Towards Accurate Estimation Of Fingerprint Ridge Orientation Using Bpnn And Ternarization”, Iosr Journal Of Computer Pp.2278-8727, Vol 13,. 2013.
  10. Liu M., Jiang X., Kot A. C., Fingerprint Reference-Point Detection. Eurasip J. Appl. Signal Process. 2005, Pp. 498-509.
  11. Bazen A. M., Gerez S. H. Systematic Methods For The Computation Of The Directional Fields And Singular Points Of Fingerprints. IEEE Trans. Pattern Anal. Mach. Intell., 2002, Vol. 24, No. 7, Pp. 905-919.
  12. Wrobel K., Doroz R., New Method For Finding A Reference Point In Fingerprint Images With The Use Of The IPAN99 Algorithm. Journal Of Medical Informatics & Technologies. Vol. 13, Pp. 59-64, 2009.
  13. Costa S. M., Fernandez F. J., Oliveira J. M., A New Paradigm On Fingerprint Classification Using Directional Image. Sibgrapi, 405, 2002.
  14. Halici U., Ongun G., Fingerprint Classification Through Self-Organizing Feature Maps Modified To Treat Uncertainties. Proc. Of The IEEE, 1996, Vol. 84, No. 10, Pp. 1497-1512.
  15. Hong L., Jain A. K., Prabhakar S., A Multichannel Approach To Fingerprint Classification. IEEE Trans. Pattern Anal.Mach.Intell. 1999, Vol. 21, No. 4, Pp. 348-359.
  16. Jain A. K., Karu K., Fingerprint Classification. Pattern Recognition, 1996, Vol. 29, No. 3, Pp. 38-44.
  17. Davide Maltoni, Dario Maio, Anil K. Jain, Salil Prabhakar, L. L. 2009. Handbook Of Fingerprint Recognition.
  18. S. Dass, “Markov Random Field Models For Directional Field And Singularity Extraction In Fingerprint Images”, IEEE Trans. Image Processing, Vol. 13, No. 10, Pp. 1358-1367, 2004.
  19. Jie Zhou, JinweiGu, “A Model-Based Method For The Computation Of Fingerprints’ Orientation Field”, IEEE Transactions On Image Processing, Vol. 13, No. 6, June 2004.
  20. Qinzhi Zhang, Hong Yan, “Fingerprint Orientation Field Interpolation Based On The Constrained Delaunay Triangulation”, International Journal Of Information And Systems Sciences Volume 3, Number 3, Pages 438–452, 2007.
  21. Kawagoe M., Tojo A., Fingerprint Pattern Classification, Pattern Recognition, 1984, 17(3): 295-303.
  22. Mehtre B.M., Murthy N.N., Kapoor S., Segmentation Of Fingerprint Images Using The Directional Image, Pattern Recognition, 1987, 20(4): 429-435.
  23. Hung D.C.D., Enhancement And Feature Purification Of Fingerprint Images, Pattern Recognition, 1993, 26(11): 1661-1672.
  24. Kovacs-Vajnazs.M.,Rovatti R., Frazzoni M., Fingerprint Ridge Distance Computation Methodologies, Pattern Recognition, 2000, 33: 69-80.
  25. Nagaty K.A, On Learning To Estimate The Block Directional Image Of A Fingerprint Using A Hierarchical Neural Network, Neural Networks, 2003, 16:133-144.
  26. Rao, K., Black, K., Type Classification Of Fingerprints: A Syntactic Approach, IEEE Transaction On Pattern Analysis And Machine Intelligence, 1980, 2(3):223–231.
  27. Halici, L., Ongun, G., Fingerprint Classification Through Selforganizing Feature Maps Modified To Treat Uncertainties, Proceedings Of The IEEE , 1996, 84(10): 1497–1512.
  28. Donahue M.J., Rokhlin S.I., On The Use Of Level Curves In Image Analysis, Image Understanding, 1993, 57(2): 185-203.
  29. Ratha N., Chen S., Jain A.K., Adaptive Flow Orientation-Based Feature Extraction In Fingerprint Images, Pattern Recognition, 1995, 28(11):1657-1672
  30. Bazen A.M., Gerez S.H., Systematic Methods For The Computation Of The Directional Fields And Singular Points Of Fingerprints, IEEE Transactions On Pattern Analysis And Machine Intelligence, 2002, 24(7): 905-919.
  31. Maio D., Maltoni D., Direct Gray-Scale Minutiae Detection In Fingerprints, IEEE Transactions On Pattern Analysis And Machine Intelligence, 1997, 19(1): 27-39.
  32. Almansa A., Lindederg T., Fingerprint Enhancement By Shape Adaptation Of Scale-Space Operators With Automatic Scale Selection, IEEE Transaction On Image Processing, 2000,9(12):2027-2042
  33. E. Zhu, J. Yin, C. Hu, And G. Zhang. A Systematic Method For Fingerprint Ridge Orientation Estimation And Image Segmentation. Pattern Recognition, 39(8):1452-1472, 2006.
  34. Kass M., Witkin A., Analyzing Orientated Pattern. Computer Vision, Graphics And Image Processing, 1987, Vol. 37, Pp. 362-397.
  35. Anil Jain AndSharathPankanti. Fingerprint Classification And Matching[Online] Available: Http://Citeseerx.Ist.Psu.Edu/Viewdoc/Download?Doi=10.1.1.83.3331&Rep=Rep1&Type=Pdf
  36. Yuan Mei, Huaijiang Sun, AndDeshen Xia (2006). A Gradient-Based Robust Method For Estimation Of Fingerprint Orientation Field, Image And Vision Computing, Volume 27, Issue 8, 2 July 2009, Pages 1169–1177
  37. D. Maio, D. Maltoni, R. Capelli, J. L. WaymanAnd A. K. Jain,“Fvc2000: Fingerprint Verification Competition”, IEEE Trans. PatternAnal. Mach. Intell., Vol. 24, No. 3, Pp. 402-412, 2002.
  38. F. Francis-LothaiAnd D. B. L. Bong, “Fingerdos: A Fingerprint Database Based On Optical Sensor,” Wseas Transactions On Information Science And Applications, Vol.12, No. 29, Pp. 297-304, 2015.
  39. J. Yang, L. Liu, T. Jiang, and Y. Fan, “A modified Gabor filter design method forfingerprint image enhancement,” Pattern Recognition Letter, Vol. 24, 2003, pp. 1805-1817.
  40. J. Cheng and J. Tian, “Fingerprint enhancement with dyadic scale-space,” PatternRecognition Letters, Vol. 25, 2004, pp. 1273-1284.
  41. Ms. Meghna B. Patel , Dr. Satyen M. Parikh , Dr. Ashok R. Patel, “Performance Improvement in Binarization for Fingerprint Recognition”, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 19, Issue 3, Ver. II (May.-June. 2017), PP 68-74 www.iosrjournals.org
Index Terms

Computer Science
Information Sciences

Keywords

Fingerprint recognition fingerprint enhancement orientation estimation