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Retina based biometric authentication using phase congruency

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Abstract

In the evolving paradigm of biometric security concepts, retinal fundus image based authentication systems is regarded as one of the most precise and secure procedures. Although previously only high security buildings could afford to utilize this technology to control access, presently due to easier availability and affordability of high-tech equipment, building and establishments with lesser security significance are also employing and relishing the benefits of this technology. This paper presents a novel biometric authentication method using retinal fundus image which uses an extremely fast optical disc detection procedure, a new and fast vessel segmentation method based on Phase congruency and some proposed new features. Most relevant literatures reveal sole usage of green channel from RGB image. To investigate suitability of other color spaces, images of luminance (Y) and green (G) channel of RGB and YCbCr color space respectively are experimented with. Phase congruency is used for vessel segmentation which uses Fourier components to detect edges and by applying pair threshold values binary image of retinal blood vessel tree is acquired. Three different features are extracted for classification purpose. Two separate experiments are performed, EXP-1 using 18 images from 6 individuals and EXP-2 using 18 (authorized) plus 547 (intruder) images, each from a separate individual. For similarity matching, 2-D Correlation Coefficient measure is used. In EXP-1 and EXP-2, maximum accuracy of 94.44 and 93.4 % were achieved in 12.81 and 12.92 s respectively with YCbCr images.

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References

  1. Simon C, Goldstein I (1935) A New Scientific Method of Identification. N Y State J Med 35:901–906

    Google Scholar 

  2. Ortega M, Marino C, Penedo MG, Blanco M, Gonzalez F (2006) Biometric authentication using digital retinal images. In: WSEAS international conference on applied computer science, pp 422–427

  3. Ortega M, Penedo MG, Rouco J, Barreira N, Carreira MJ (2009) Personal verification based on extraction and characterization of retinal feature points. J Vis Lang Comput 80–90

  4. Marino C, Penedo M, Carreira M, Gonzalez F (2003) Retinal angiography based authentication. Lecture Notes in Computer Science, vol 2905. Springer, Berlin, pp 306–313

  5. Marino C, Penedo MG, Penas M, Carreira MJ, Gonzalez F (2006) Personal authentication using digital retinal images. Pattern Anal Appl 9:21–33

    Article  MathSciNet  Google Scholar 

  6. Alonso-Montes C, Ortega M, Penedo MG, Vilarino DL (2008) Pixel parallel vessel tree extraction for a personal authentication system. In: Circuits and systems, 2008. ISCAS 2008. IEEE International Symposium, pp 1596–1599

  7. Latha L, Pabitha M, Thangasamy S (2010) A novel method for person authentication using retinal images. In: International conference on innovative computing technologies (ICICT), 2010, pp 1–6

  8. Raiyan SMK, Rahman R, Habib M, Khan MR (2004) Person identification by retina pattern matching. In: ICECE, 2004, pp 522–525

  9. Farzin H, Abrishami-Moghaddam H, Moin M (2008) A novel retinal identification system. EURASIP J Adv Signal Process. doi:10.1155/2008/280635

  10. Oinonen H, Forsvik H, Ruusuvuori P, Yli-Harja O, Voipio V, Huttunen H, (2010) Identity verification based on vessel matching from fundus images. In: 17th IEEE international conference on, image processing (ICIP), September, 2010, pp: 4089–4092

  11. Frangi AF, Frangi RF, Niessen WJ, Vincken KL, Viergever MA (1998) Multiscale vessel enhancement filtering. In: Image computing and computer-assisted interventation MICCAI98. Springer, Berlin, pp 130–137

  12. Fukuta K, Nakagawa T, Hayashi Y, Hara T, Fujita H (2008) Personal identification based on blood vessels of retinal fundus images. In: Proceedings of SPIE, vol 6914, pp. 69141V-1–69141V-9

  13. Serra J (1986) Introduction to mathematical morphology. Comput Vis Graph Image Process 35(3):283–305

    Article  MATH  Google Scholar 

  14. Sternberg SR (1986) Grayscale morphology. Comput Vis Graph Image Process 35(3):333–355

    Article  MathSciNet  Google Scholar 

  15. Fukuta K, Nakagawa T, Hatanaka Y, Hayashi Y, Hara T, Fujita H (2008) Similarity measurement using blood-vessel structure in retinal fundus images for biometrics. J Inst Image Electron Eng Jpn 37(6):1020–1028

    Google Scholar 

  16. Barkhoda W, Akhlaqian F, Amiri MD, Nouroozzadeh MS (2011) Retina identification based on the pattern of blood vessels using fuzzy logic. EURASIP J Adv Signal Process 2011:113

    Article  Google Scholar 

  17. Staal J, Abramoff MD, Niemeijer M, Viergever MA, van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Image 23(4):501–509

    Google Scholar 

  18. Tang Y, Yan P, Yuan Y, Li X (2011) Single-image super-resolution via local learning. Int J Mach Learn Cybern 2(1):15–23

    Article  Google Scholar 

  19. Kundu MK, Chowdhury M, Banerjee M (2012) Interactive image retrieval using M-band wavelet, earth mover’s distance and fuzzy relevance feedback. Int J Mach Learn Cybern 3(4):285–296

    Article  Google Scholar 

  20. DRIVE: digital retinal image for vessel extraction. http://www.isi.uu.nl/Research/Databases/DRIVE/

  21. MESSIDOR: digital retinal images. http://messidor.crihan.fr/download.php

  22. Otsu N (1979) A threshold selection method from gray-level histograms. Proc IEEE Trans Syst Mach Cybern 9(1):62–66

    Article  MathSciNet  Google Scholar 

  23. Ahmed MI, Amin MA, (2012) High speed detection of optical disc in retinal fundus image. J Signal Image Video Process (Springer)

  24. Amin MA, Yan H (2010) High speed detection of retinal blood vessels in retinal fundus image. 15(6):1217–1230 (Springer-Verlag)

  25. Kovesi P (1999) Image features from phase congruency. Videre J Comput Vis 1(3) (the MIT Press)

  26. Ahmed MI, Amin MA, Poon B, Yan H (2011) Biometric authentication using mean intensity spread of optical disc in retinal images. In: International conference on information technology and application (ICITA), pp 31–35

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Acknowledgments

This work is supported by a grant from the Citygroup Bangladesh (www.citygroup.com.bd). We would like to thank three anonymous reviewers for their helpful comments and suggestions.

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Authors and Affiliations

  1. Computer Vision and Cybernetics Group, CSE, Independent University, Bangladesh (IUB), Dhaka, Bangladesh

    M. Islamuddin Ahmed, M. Ashraful Amin & Bruce Poon

  2. Depertment of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong

    M. Ashraful Amin & Hong Yan

  3. School of Electrical and Information Engineering, University of Sydney, Sydney, NSW, 2006, Australia

    Bruce Poon & Hong Yan

Authors
  1. M. Islamuddin Ahmed
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  2. M. Ashraful Amin
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  3. Bruce Poon
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  4. Hong Yan
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Correspondence to M. Ashraful Amin.

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Ahmed, M.I., Amin, M.A., Poon, B. et al. Retina based biometric authentication using phase congruency. Int. J. Mach. Learn. & Cyber. 5, 933–945 (2014). https://doi.org/10.1007/s13042-013-0179-z

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  • DOI: https://doi.org/10.1007/s13042-013-0179-z

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