Detection of regularities in the parameters of the ateb­gabor method for biometric image filtration

Authors

DOI:

https://doi.org/10.15587/1729-4061.2019.154862

Keywords:

Gabor filter, Ateb functions, biometric system, image processing, deflection of the Gaussian kernel

Abstract

The study has developed a new image filtering method based on Ateb-Gabor. The method involves the well-known Gabor filter that helps convert images with clear contours. Therefore, this method is applicable to biometric images where the creation of clear contours is particularly relevant. During Gabor filtration, the image is transformed by multiplying the harmonic function by the Gaussian function. Ateb-functions are a generalization of elementary trigonometry and, accordingly, have greater functionality for known harmonic functions.

Ateb-Gabor filtering makes it possible to change the intensity of the whole image as well as intensity in certain ranges and thus gives more contrast to certain areas of an image. Ateb-functions are changed by two rational parameters, and this provides flexible control of the filtering. Research has been made on the properties of Ateb-functions as well as the possibility of changing the amplitude and the frequency of alternations when filtering by the Ateb-Gabor. The development of filtration is based on a two-dimensional Ateb-Gabor; its dependencies have been analyzed and appropriate experiments have been performed. The relationship between the frequency and the width of the Ateb-Gabor filter has been determined, which has made it possible to produce filters for finding edges of objects with different frequencies and sizes.

Appropriate software has been developed for python filtering without the use of third-party libraries that are associated with image processing. Fingerprints were filtered using the developed Ateb-Gabor filter. The effectiveness of its use is shown to consist in forming more combinations of processed images. The results of numerous experiments demonstrate a successful selection of edges in an image based on the parameters of the Ateb-Gabor filter.

Author Biographies

Mariya Nazarkevych, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

Doctor of Technical Sciences, Professor

Department of Publishing Information Technologies

Oleg Riznyk, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

PhD, Associate Professor

Department of Publishing Information Technologies

Volodymyr Samotyy, Cracow University of Technology Warszawska str., 24, Cracow, Poland, 31-155 Lviv State University of Life Safety Kleparivska str., 35, Lviv, Ukraine, 79007

Doctor of Technical Sciences, Professor

Department of Control and Information Technologies

Department of Information Security Management

Ulyana Dzelendzyak, Lviv Polytechnic National University S. Bandery str., 12, Lviv, Ukraine, 79013

PhD, Associate Professor

Department of Computerized Automation Systems

References

  1. Biometrics Market and Industry Report 2009–2014 (2007). International Biometric Group.
  2. Maltoni, D., Maio, D., Jain, A. K., Prabhakar, S. (2009). Handbook of Fingerprint Recognition. Springer, 494. doi: https://doi.org/10.1007/978-1-84882-254-2
  3. Lee, T. S. (1996). Image representation using 2D Gabor wavelets. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18 (10), 959–971. doi: https://doi.org/10.1109/34.541406
  4. Sebe, N. (2001). Image retrieval using wavelet-based salient points. Journal of Electronic Imaging, 10 (4), 835. doi: https://doi.org/10.1117/1.1406945
  5. Nazarkevych, M., Oliarnyk, R., Troyan, O., Nazarkevych, H. (2016). Data protection based on encryption using Ateb-functions. 2016 XIth International Scientific and Technical Conference Computer Sciences and Information Technologies (CSIT). doi: https://doi.org/10.1109/stc-csit.2016.7589861
  6. Senik, P. M. (1970). Inversion of the incomplete beta function. Ukrainian Mathematical Journal, 21 (3), 271–278. doi: https://doi.org/10.1007/bf01085368
  7. Sree Vidya, B., Chandra, E. (2018). Multimodal biometric hashkey cryptography based authentication and encryption for advanced security in cloud. Biomedical Research. doi: https://doi.org/10.4066/biomedicalresearch.29-17-1766
  8. Russell, S. J., Norvig, P. (2016). Artificial intelligence: a modern approach. Malaysia; Pearson Education Limited.
  9. Meitram, R., Choudhary, P. (2018). Palm Vein Recognition Based on 2D Gabor Filter and Artificial Neural Network. Journal of Advances in Information Technology, 9 (3), 68–72. doi: https://doi.org/10.12720/jait.9.3.68-72
  10. Akin, C., Kacar, U., Kirci, M. (2018). A Multi-Biometrics for Twins Identification Based Speech and Ear. arXiv. Available at: https://arxiv.org/ftp/arxiv/papers/1801/1801.09056.pdf
  11. Arif, A., Li, T., Cheng, C.-H. (2017). Blurred fingerprint image enhancement: algorithm analysis and performance evaluation. Signal, Image and Video Processing, 12 (4), 767–774. doi: https://doi.org/10.1007/s11760-017-1218-0
  12. Andrew, A. M. (2004). Handbook of fingerprint recognition, by Davide Maltoni, Dario Maio, Anil K. Jain and Salil Probhakar, Springer, New York, 2003, hardback, xii + 348 pp., with DVD-ROM, ISBN 0-387-95431-7 (£46.00). Robotica, 22 (5), 587–588. doi: https://doi.org/10.1017/s026357470422094x
  13. Gottschlich, C. (2012). Curved-Region-Based Ridge Frequency Estimation and Curved Gabor Filters for Fingerprint Image Enhancement. IEEE Transactions on Image Processing, 21 (4), 2220–2227. doi: https://doi.org/10.1109/tip.2011.2170696
  14. Gopi, K. (2012). Fingerprint Recognition Using Gabor Filter And Frequency Domain Filtering. IOSR Journal of Electronics and Communication Engineering, 2 (6), 17–21. doi: https://doi.org/10.9790/2834-0261721
  15. Bartunek, J. S., Nilsson, M., Sallberg, B., Claesson, I. (2013). Adaptive Fingerprint Image Enhancement With Emphasis on Preprocessing of Data. IEEE Transactions on Image Processing, 22 (2), 644–656. doi: https://doi.org/10.1109/tip.2012.2220373
  16. Mei, Y., Chen, S., Zhou, Y., Zhao, B. (2014). Orthogonal curved-line Gabor filter for fast fingerprint enhancement. Electronics Letters, 50 (3), 175–177. doi: https://doi.org/10.1049/el.2013.2619
  17. Kassis, M., El-Sana, J. (2016). Scribble Based Interactive Page Layout Segmentation Using Gabor Filter. 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR). doi: https://doi.org/10.1109/icfhr.2016.0016
  18. Jones, J. P., Palmer, L. A. (1987). An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58 (6), 1233–1258. doi: https://doi.org/10.1152/jn.1987.58.6.1233
  19. Grigorescu, S. E., Petkov, N., Kruizinga, P. (2002). Comparison of texture features based on Gabor filters. IEEE Transactions on Image Processing, 11 (10), 1160–1167. doi: https://doi.org/10.1109/tip.2002.804262
  20. Ali, M. A. M., Tahir, N. M. (2014). Half iris Gabor based iris recognition. 2014 IEEE 10th International Colloquium on Signal Processing and Its Applications. doi: https://doi.org/10.1109/cspa.2014.6805765
  21. Bazen, A. M., Gerez, S. H. (2003). Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recognition, 36 (8), 1859–1867. doi: https://doi.org/10.1016/s0031-3203(03)00036-0
  22. Petrovic, V. S., Xydeas, C. S. (2004). Gradient-Based Multiresolution Image Fusion. IEEE Transactions on Image Processing, 13 (2), 228–237. di: https://doi.org/10.1109/tip.2004.823821
  23. Struble, R. A. (2018). Nonlinear differential equations. Courier Dover Publications, 288.
  24. Senik, P. M., Vozniy, A. M. (1973). Chislennoe obrashchenie odnogo klassa nepolnoy Beta-funkcii. Matematicheskaya fizika, 14, 160–164.
  25. Gricik, V. V., Nazarkevich, M. A. (2007). Mathematical models algorythms and computation of Ateb-functions. Dopovidi NAN Ukraini Seriji A, 12, 37–43.
  26. Nazarkevych, M., Hladets, A. (2009). Development of software package for the encryption of electronic documents means Ateb-functions. Bulletin of the Lviv Polytechnic National University, Computer Science and Information Technology, 638, 55–61.
  27. Nazarkevych, M., Oliiarnyk, R., Nazarkevych, H., Kramarenko, O., Onyshschenko, I. (2016). The method of encryption based on Ateb-functions. 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP). doi: https://doi.org/10.1109/dsmp.2016.7583523
  28. Maltoni, D., Maio, D., Jain, A. K., Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer. doi: https://doi.org/10.1007/978-1-84882-254-2
  29. Maltoni, D., Maio, D., Jain, A. K., Prabhakar, S. (2003). Handbook of Fingerprint Recognition. Springer.
  30. Fingerprint matching using minutiae and texture features (2002). Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205). doi: https://doi.org/10.1109/icip.2001.958106
  31. Matsumoto, T., Matsumoto, H., Yamada, K., Hoshino, S. (2002). Impact of artificial "gummy" fingers on fingerprint systems. Optical Security and Counterfeit Deterrence Techniques IV. doi: https://doi.org/10.1117/12.462719
  32. Riznik, O., Yurchak, I., Vdovenko, E., Korchagina, A. (2010). Model of stegosystem images on the basis of pseudonoise codes. In Perspective Technologies and Methods in MEMS Design (MEMSTECH), 2010 Proceedings of VIth International Conference.
  33. Fries, M., Fischbach, R., Houdeau, D. (2002). U.S. Pat. No. 6.347.040. Washington, DC: U.S. Patent and Trademark Office.

Downloads

Published

2019-01-22

How to Cite

Nazarkevych, M., Riznyk, O., Samotyy, V., & Dzelendzyak, U. (2019). Detection of regularities in the parameters of the ateb­gabor method for biometric image filtration. Eastern-European Journal of Enterprise Technologies, 1(2), 57–65. https://doi.org/10.15587/1729-4061.2019.154862

Issue

Vol. 1 No. 2 (2019): Information technology. Industry control systems

Section

Information technology

License

Copyright (c) 2019 Mariya Nazarkevych, Oleg Riznyk, Volodymyr Samotyy, Ulyana Dzelendzyak

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.