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Effectiveness evaluation of iris segmentation by using geodesic active contour (GAC)

  • Yuan-Tsung Chang
  • Timothy K. Shih
  • Yung-Hui Li
  • W. G. C. W. Kumara
Article
  • 49 Downloads

Abstract

A novel iris segmentation technique based on active contour is proposed in this paper. Our approach uses innovative algorithms, including two important ones, pupil segmentation and iris circle calculation. With our algorithms, we are able to find the center position and radius of pupil correctly and segment the iris precisely. The accuracy of our proposed method for ICE dataset is around 92% and also reached high accuracy level of 79% for UBIRIS. Our results demonstrate that the proposed iris segmentation method can perform well with high accuracy and better efficacy for Iris segmentation in images. Through a relatively high-performance algorithm to further cut up the round out the picture of the pupil conversion cutting growth square picture in order to make the judgment for biometric applications.

Keywords

Iris segmentation Active contour Biometrics 

References

  1. 1.
    Ratha NK, Connell JH, Pankanti S (2015) Big data approach to biometric-based identity analytics. IBM J Res Dev 59(2/3):4:1–4:11CrossRefGoogle Scholar
  2. 2.
    Liu C, Petroski B, Cordone G, Torres G, Schuckers S (2015) Iris matching algorithm on many-core platforms. In: 2015 IEEE International Symposium on Technologies for Homeland Security (HST), pp 1–6Google Scholar
  3. 3.
    Fernández A, Gómez Á, Lecumberry F, Pardo Á, Ramírez I (2015) Pattern recognition in Latin America in the ‘big data’ era. Pattern Recognit 48(4):1185–1196CrossRefGoogle Scholar
  4. 4.
    Guo J-M, Hsia C-H, Liu Y-F, Yu J-C, Chu M-H, Le T-N (2012) Contact-free hand geometry-based identification system. Expert Syst Appl 39(14):11728–11736CrossRefGoogle Scholar
  5. 5.
    Hsia CH, Dai YJ, Chen SL, Lin TL, Shen J (2018) A gait sequence analysis for IP camera using a modified LBP. J Internet Technol 19:451–458Google Scholar
  6. 6.
    Hsia C-H (2018) New verification method for finger-vein recognition system. IEEE Sens J 18(2):790–797CrossRefGoogle Scholar
  7. 7.
    Guo J-M, Liu Y-F, Hsia C-H, Su S-Y, Lee H (2014) Sample space dimensionality refinement for symmetrical object detection. IEEE Trans Inf Forensics Secur 9(11):1953–1961CrossRefGoogle Scholar
  8. 8.
    Hung JCS, Chiang KH, Huang YH, Lin KC (2017) Augmenting teacher–student interaction in digital learning through affective computing. Multimed Tools Appl 76(18):18361–18386CrossRefGoogle Scholar
  9. 9.
    Lee MF, Chen GS, Hung JC, Lin KC, Wang JC (2016) Data mining in emotion color with affective computing. Multimed Tools Appl 75(23):15185–15198CrossRefGoogle Scholar
  10. 10.
    Proena H, Alexandre LA (2005) UBIRIS: a noisy iris image database. In: Proceedings International Conference Image Analysis Processing (ICIAP), 2005. vol 1, pp 970–977 [Online]. Available: http://iris.di.ubi.pt
  11. 11.
  12. 12.
    Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis Dev 22(1):61–79CrossRefzbMATHGoogle Scholar
  13. 13.
    Shah S, Ross A (2009) Iris segmentation using geodesic active contours. IEEE Trans Inf Forensics Secur 4(4):824–836CrossRefGoogle Scholar
  14. 14.
    Ma L, Tan T, Wang Y, Zhang D (2004) Efficient iris recognition by characterizing key local variations. IEEE Trans Image Process 13(6):739–750CrossRefGoogle Scholar
  15. 15.
    Xu Z, Shi P (2006) A robust and accurate method for pupil features extra. In: 18th International Conference on Pattern Recognition, 2006. ICPR 2006. vol 1, pp 437–440Google Scholar
  16. 16.
    Zuo J, Kalka ND, Schmid NA (2006) A robust iris segmentation procedure for unconstrained subject presentation. In: 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, pp 1–6Google Scholar
  17. 17.
    Chouhan B, Shukla S (2011) Comparative analysis of robust iris recognition system using log gabor wavelet and Laplacian of Gaussian filter. Int J Comput Sci Commun IJCSC 2(1):239–242Google Scholar
  18. 18.
    Ross A, Shah S (2006) Segmenting non-ideal irises using geodesic active contours. In: 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, pp 1–6Google Scholar
  19. 19.
    Proença H, Alexandre LA (2006) Iris segmentation methodology for non-cooperative recognition. IEEE Proc Vis Image Signal Process 153:199–205CrossRefGoogle Scholar
  20. 20.
    Mohammadi Arvacheh E (2006) A study of segmentation and normalization for iris recognition systemsGoogle Scholar
  21. 21.
    Jarjes AA, Wang K, Mohammed GJ (2010) Iris localization: detecting accurate pupil contour and localizing limbus boundary. In: 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR), vol 1, pp 349–352Google Scholar
  22. 22.
    Proenca H (2010) Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans Pattern Anal Mach Intell 32(8):1502–1516CrossRefGoogle Scholar
  23. 23.
    Subban R, Susitha N, Mankame DP (2017) Efficient iris recognition using Haralick features based extraction and fuzzy particle swarm optimization. Cluster Comput 2017:1–12Google Scholar
  24. 24.
    Donida Labati R, Genovese A, Muñoz E, Piuri V, Scotti F, Sforza G (2016) Computational intelligence for biometric applications: a survey. Int J Comput 15(1):42–53Google Scholar
  25. 25.
    Roy DA, Soni US (2016) Analysis of iris segmentation using circular Hough transform and Daughman’s method. i-manager’s J Image Process 3(1):29CrossRefGoogle Scholar
  26. 26.
    Pune (2016) An amalgamated strategy for iris recognition employing neural network and hamming distance. In: Advances in intelligent systems and computing, vol. 434. SpringerGoogle Scholar
  27. 27.
    Jain Y (2017) A comparative analysis of iris and palm print based unimodal and multimodal biometric systems. In: Innovations in computer science and engineering, pp 297–306Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringNational Central UniversityTaoyuanTaiwan

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