Advertisement

Multimedia Tools and Applications

, Volume 78, Issue 10, pp 14045–14065 | Cite as

F-FID: fast fuzzy-based iris de-noising for mobile security applications

  • Silvio BarraEmail author
  • Carmen Bisogni
  • Michele Nappi
  • Stefano Ricciardi
Article
  • 69 Downloads

Abstract

Once confined to indoor biometric applications depending on dedicated acquisition devices, recently the iris has proved to be a suitable biometric for in-the-wild ubiquitous person authentication, thanks to continuously improving image capturing/processing performances provided by last generations of smartphones. In this mobile context, the efficiency of the whole processing pipeline represents a crucial aspect of any practical application and the segmentation task, that is deeply affected by noisy iris images may become a serious bottleneck. This work presents F-FID, an effective and time-wise efficient approach to de-noising of iris images by means of a fuzzy controller without sacrificing their resolution and saliency. The experiments, specifically conducted on the MICHE dataset, confirm that the proposed method provides segmentation accuracy comparable to that achieved by state of the art algorithms, while requiring less than twenty percent of their average computing time.

Keywords

Iris segmentation Noise removal Fuzzy controller Gini index 

Notes

References

  1. 1.
    Abate A, Barra S, Gallo L, Narducci F (2016) Skipsom: skewness kurtosis of iris pixels in self organizing maps for iris recognition on mobile devices. In: 2016 23rd international conference on pattern recognition (ICPR), pp 155–159.  https://doi.org/10.1109/ICPR.2016.7899625
  2. 2.
    Abate AF, Barra S, D’Aniello F, Narducci F (2017) Two-tier image features clustering for iris recognition on mobile. In: Petrosino A, Loia V, Pedrycz W (eds) Fuzzy logic and soft computing applications. Springer International Publishing, Cham, pp 260–269Google Scholar
  3. 3.
    Abate AF, Barra S, Fenu G, Nappi M, Narducci F (2017) A lightweight mamdani fuzzy controller for noise removal on iris images. In: Battiato S, Gallo G, Schettini R, Stanco F (eds) Image analysis and processing - ICIAP 2017. Springer International Publishing, Cham, pp 93–103Google Scholar
  4. 4.
    Abate AF, Barra S, Gallo L, Narducci F (2017) Kurtosis and skewness at pixel level as input for SOM networks to iris recognition on mobile devices. Pattern Recogn Lett 91:37–43.  https://doi.org/10.1016/j.patrec.2017.02.002 CrossRefGoogle Scholar
  5. 5.
    Abate AF, Nappi M, Ricciardi S (2017) I-am: implicitly authenticate me person authentication on mobile devices through ear shape and arm gesture. IEEE Trans Syst Man Cybern Syst Hum PP(99):1–13.  https://doi.org/10.1109/TSMC.2017.2698258 Google Scholar
  6. 6.
    Abate A, Barra S, Casanova A, Fenu G, Marras M (2018) Iris quality assessment: a statistical approach for biometric security applications. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11161 LNCS:270–278.  https://doi.org/10.1007/978-3-030-01689-0_21 Google Scholar
  7. 7.
    Barpanda SS, Sa PK, Marques O, Majhi B, Bakshi S (2017) Iris recognition with tunable filter bank based feature. Multimed Tools Appl.  https://doi.org/10.1007/s11042-017-4668-z
  8. 8.
    Barra S, De Marsico M, Cantoni V, Riccio D (2014) Using mutual information for multi-anchor tracking of human beings. In: Cantoni V, Dimov D, Tistarelli M (eds) Biometric authentication. Springer International Publishing, Cham, pp 28–39Google Scholar
  9. 9.
    Barra S, De Marsico M, Nappi M, Riccio D (2014) Complex numbers as a compact way to represent scores and their reliability in recognition by multi-biometric fusion. Int J Pattern Recognit Artif Intell 28(7).  https://doi.org/10.1142/S0218001414600039
  10. 10.
    Barra S, Casanova A, Narducci F, Ricciardi S (2015) Ubiquitous iris recognition by means of mobile devices. Pattern Recogn Lett 57:66–73. Mobile Iris {CHallenge} Evaluation part I (MICHE I).  https://doi.org/10.1016/j.patrec.2014.10.011.
  11. 11.
    Bowyer KW, Hollingsworth KP, Flynn PJ (2013) A survey of iris biometrics research: 2008–2010. In: Handbook of iris recognition. Springer, pp 15–54Google Scholar
  12. 12.
    Chaskar U, Sutaone M, Shah N, et al. (2012) Iris image quality assessment for biometric applicationGoogle Scholar
  13. 13.
    Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv:1412.7062
  14. 14.
    Clarke N, Furnell S (2007) Advanced user authentication for mobile devices. Comput Secur 26(2):109–119.  https://doi.org/10.1016/j.cose.2006.08.008. http://www.sciencedirect.com/science/article/pii/S0167404806001428
  15. 15.
    Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161CrossRefGoogle Scholar
  16. 16.
    Daugman J (2001) Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int J Comput Vis 45(1):25–38.  https://doi.org/10.1023/A:1012365806338. Cited By 286CrossRefzbMATHGoogle Scholar
  17. 17.
    Daugman J (2009) How iris recognition works.  https://doi.org/10.1016/B978-0-12-374457-9.00025-1. Cited By 3
  18. 18.
    De Marsico M, Nappi M, Riccio D, Wechsler H (2015) Mobile iris challenge evaluation (miche)-i, biometric iris dataset and protocols. Pattern Recogn Lett 57:17–23CrossRefGoogle Scholar
  19. 19.
    De Marsico M, Nappi M, Narducci F, Proença H (2018) Insights into the results of miche i - mobile iris challenge evaluation. Pattern Recogn 74:286–304.  https://doi.org/10.1016/j.patcog.2017.08.028 CrossRefGoogle Scholar
  20. 20.
    Du Y, Arslanturk E, Zhou Z, Belcher C (2011) Video-based noncooperative iris image segmentation. IEEE Trans Syst Man Cybern B Cybern 41 (1):64–74.  https://doi.org/10.1109/TSMCB.2010.2045371 CrossRefGoogle Scholar
  21. 21.
    El-Zaart A (2010) Skin images segmentation. J Comput Sci 6(2):217–223CrossRefGoogle Scholar
  22. 22.
    Elrefaei LA, Hamid DH, Bayazed AA, Bushnak SS, aasher SY (2017) Developing iris recognition system for smartphone security. Multimed Tools Appl.  https://doi.org/10.1007/s11042-017-5049-3
  23. 23.
    Haindl M, Krupička M (2015) Unsupervised detection of non-iris occlusions. Pattern Recogn Lett 57:60–65.  https://doi.org/10.1016/j.patrec.2015.02.012. Mobile Iris {CHallenge} Evaluation part I (MICHE I)
  24. 24.
    Hofbauer H, Alonso-Fernandez F, Wild P, Bigun J, Uhl A (2014) A ground truth for iris segmentation. In: 2014 22nd international conference on pattern recognition, pp 527–532.  https://doi.org/10.1109/ICPR.2014.101
  25. 25.
    Jarjes AA, Wang K, Mohammed GJ (2011) Improved greedy snake model for detecting accurate pupil contour. In: 2011 3rd international conference on advanced computer control, pp 515–519.  https://doi.org/10.1109/ICACC.2011.6016466
  26. 26.
    Jayalakshmi S, Sundaresan M (2013) A survey on iris segmentation methods. In: 2013 international conference on pattern recognition, informatics and mobile engineering, pp 418–423.  https://doi.org/10.1109/ICPRIME.2013.6496513
  27. 27.
    Jeong DS, Hwang JW, Kang BJ, Park KR, Won CS, Park DK, Kim J (2010) A new iris segmentation method for non-ideal iris images. Image Vision Comput 28(2):254–260.  https://doi.org/10.1016/j.imavis.2009.04.001 CrossRefGoogle Scholar
  28. 28.
    Kumar V, Gupta P (2012) Importance of statistical measures in digital image processing. Int J Emerg Technol Adv Eng 2(8):56–62Google Scholar
  29. 29.
    Labati RD, Genovese A, Piuri V, Scotti F (2012) Iris segmentation: state of the art and innovative methods. Springer, Berlin, pp 151–182.  https://doi.org/10.1007/978-3-642-28457-1_8 Google Scholar
  30. 30.
    Lerman RI, Yitzhaki S (1984) A note on the calculation and interpretation of the gini index. Econ Lett 15(3-4):363–368CrossRefGoogle Scholar
  31. 31.
    Liu Z, Li X, Luo P, Loy CC, Tang X (2015) Semantic image segmentation via deep parsing network. In: Proceedings of the IEEE international conference on computer vision, pp 1377–1385Google Scholar
  32. 32.
    Makinana S, Malumedzha T, Nelwamondo FV (2014) Iris image quality assessment based on quality parameters. In: Nguyen NT, Attachoo B, Trawiński B, Somboonviwat K (eds) Intelligent information and database systems. Springer International Publishing, Cham, pp 571–580Google Scholar
  33. 33.
    Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7(1):1–13CrossRefzbMATHGoogle Scholar
  34. 34.
    Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294CrossRefGoogle Scholar
  35. 35.
    Proenca H (2010) Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans Pattern Anal Mach Intell 32(8):1502–1516.  https://doi.org/10.1109/TPAMI.2009.140 CrossRefGoogle Scholar
  36. 36.
    Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA (2010) The ubiris. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans Pattern Anal Mach Intell 32(8):1529–1535CrossRefGoogle Scholar
  37. 37.
    Rad RM, Attar A, Atani RE (2013) A comprehensive layer based encryption method for visual data. Int J Signal Process Image Process Pattern Recogn 6(1):37–48Google Scholar
  38. 38.
    Ross A, Shah S (2006) Segmenting non-ideal irises using geodesic active contours.  https://doi.org/10.1109/BCC.2006.4341625. Cited By 14
  39. 39.
    Sheshinski E, et al. (1972) Relation between a social welfare function and the gini index of income inequality. J Econ Theory 4(1):98–100MathSciNetCrossRefGoogle Scholar
  40. 40.
    Tian QC, Pan Q, Cheng YM, Gao QX (2004) Fast algorithm and application of hough transform in iris segmentation: 3977–3980. Cited By 41Google Scholar
  41. 41.
    Vatsa M, Singh R, Noore A (2008) Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans Syst Man Cybern B Cybern 38(4):1021–1035.  https://doi.org/10.1109/TSMCB.2008.922059 CrossRefGoogle Scholar
  42. 42.
    Wan Y, Clutter ML, Mei B, Siry JP (2015) Assessing the role of U.S. timberland assets in a mixed portfolio under the mean-conditional value at risk framework. Forest Policy Econ 50:118–126.  https://doi.org/10.1016/j.forpol.2014.06.002
  43. 43.
    Wang N, Li Q, Abd El-Latif AA, Zhang T, Niu X (2014) Toward accurate localization and high recognition performance for noisy iris images. Multimed Tools Appl 71(3):1411–1430.  https://doi.org/10.1007/s11042-012-1278-7 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Mathematics and Computer SciencesUniversity of CagliariCagliariItaly
  2. 2.Department of Computer SciencesUniversity of SalernoSalernoItaly
  3. 3.Department of Biosciences and TerritoryUniversity of MoliseMoliseItaly

Personalised recommendations