Skip to main content
SpringerLink
Log in

Personal authentication using digital retinal images

  • Theoretical Advances
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

Traditional authentication (identity verification) systems, used to gain access to a private area in a building or to data stored in a computer, are based on something the user has (an authentication card, a magnetic key) or something the user knows (a password, an identification code). However, emerging technologies allow for more reliable and comfortable user authentication methods, most of them based on biometric parameters. Much work could be found in the literature about biometric-based authentication, using parameters like iris, voice, fingerprints, face characteristics, and others. In this work a novel authentication method is presented and preliminary results are shown. The biometric parameter employed for the authentication is the retinal vessel tree, acquired through retinal digital images, i.e., photographs of the fundus of the eye. It has already been asserted by expert clinicians that the configuration of the retinal vessels is unique for each individual and that it does not vary during his life, so it is a very well-suited identification characteristic. Before the verification process can be executed, a registration step is required to align both the reference image and the picture to be verified. A fast and reliable registration method is used to perform this step, so that the whole authentication process takes about 0.3 s.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Daugman JG (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans PAMI 15(11):1148–1161

    Google Scholar 

  2. Daugman JG, Williams GO (1996) A proposed standard for biometric decidability. In: Proceedings of the CardTech/SecureTech conference, pp 223–234

  3. Federal Bureau of Investigation (1984) The science of fingerprints: classifications and uses, Technical report, US Government Printing Office, Washington, DC

  4. Jain A, Hong L, Pankanti S, Bolle R. An identity authentication system using fingerprints. Proc IEEE 85(9)

  5. Maio D, Maltoni D (1997) Direct gray-scale minutiae detection in fingerprints. IEEE Trans PAMI 19(1):27–40

    Google Scholar 

  6. Zunkel R (1999) Hand geometry based verification, biometrics: personal identification in networked society, Ch. 4. Kluwer, Dordrecht, pp 87–101

  7. Bunney C (1997) Survey: face recognition systems. Biometric Technol Today 5(4):8–12

    Google Scholar 

  8. Zhao W, Chellappa R, Rosenfeld A, Phillips P (2000) Face recognition: a literature survey, Technical report, National Institute of Standards and Technology

  9. Bigüin J, Chollet C, Borgefors G (eds) (1997) Proceedings of the 1st international conference on audio- and video-based biometric person authentication, Crans-Montana, Switzerland

  10. Campbell JP (1999) Speaker recognition, biometrics: personal identification in networked society, Ch. 8. Kluwer, Dordrecht, pp 165–189

  11. Verlinde P, Chollet G, Acheroy M (2000) Multi-modal identity verification using expert fusion. Inform Fusion 1(1):17–33

    Article  Google Scholar 

  12. Brunelli R, Falavigna D (1995) Personal identification using multiple cues. IEEE Trans PAMI 17(10):955–966

    Google Scholar 

  13. Sidlauskas DP (1988) 3D hand profile identification apparatus. US Patent 4736203

  14. Johnson JC, Hill RB (1990) Eye fundus optical scanner system and method. US Patent 5532771

  15. Daugman J (1994) Biometric personal identification system based on iris analysis. US Patent 5,291.560

    Google Scholar 

  16. Sibbald A (1997) Method and apparatus for fingerprint characterization and recognition using auto-correlation patterns. US Patent 5633947

  17. Simon C, Goldstein I (1935) A new scientific method of identification. NY State J Med 35(18):901–906

    Google Scholar 

  18. Tower P (1955) The fundus oculi in monozygotic twins: report of six pairs of identical twins. Arch Ophthalmol 54:225–239

    Google Scholar 

  19. Clermont A, Aiello LP, Mori F, Aiello LM, Bursell S (1997) Vascular endothelial growth factor and severity of nonproliferative diabetic retinopathy mediate retinal hemodynamics in vivo: a potential role for vascular endothelial growth factor in the progression of nonproliferative diabetic retinopathy. Am J Ophthalmol 124(4):433–446

    PubMed  Google Scholar 

  20. Aiello L, Gardner T, King G, Blankenship G, Cavallerano J, Ferris F, Klein R (1998) Diabetic retinopathy. Diabetes Care 1(21):143–156

    Google Scholar 

  21. Inder T, Clemett R, AN et al (1997) High iron status in very low birth weight infants is associated with an increased risk of retinopathy of prematurity. J Pediatr 4(131):541–544

    Google Scholar 

  22. Buzz Hill R (1999) Retina identification, biometrics: personal identification in networked society, Ch. 6. Kluwer, Dordrecht, pp 120–141

  23. Lloret D, Mariño C, Serrat J, López AM, Villanueva JJ (2001) Landmark-based registration of full SLO video sequences. In: Proceedings of the IX Spanish symposium on pattern recognition and image analysis, vol I, 2001, pp 189–194

  24. Lloret D, López A, Serrat J, Villanueva J (1999) Creaseness-based CT and MR registration: comparison with the mutual information method. J Electron Imaging 8(3):255–262

    Article  Google Scholar 

  25. Brown LG (1992) A survey of image registration techniques. ACM Comput Surv 24(4):325–376

    Article  Google Scholar 

  26. den Elsen P, Pol E, Viergever MA (1993) Medical image matching—a review with classification. IEEE Eng Med Biol 1(2):26–39

    Article  Google Scholar 

  27. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1):1–36

    Article  PubMed  Google Scholar 

  28. Zitová B, Flusser J (2003) Image registration methods: a survey. Image Vision Comput 21(11):977–1000

    Article  Google Scholar 

  29. Markov MS, Rylander HG, Welch AJ (1993) Real-time algorithm for retinal tracking. IEEE Trans Biomed Eng 40(12):1269–1281

    Article  PubMed  Google Scholar 

  30. López A, Lumbreras F, Serrat J, Villanueva J (1999) Evaluation of methods for ridge and valley detection. IEEE Trans Pattern Anal Mach Intell 21(4):327–335

    Article  Google Scholar 

  31. Romeny BTH, Florack L (1993) A mustiscale geometric model of human vision. In: The perception of visual information. Springer, Berlin Heidelberg New York, pp 73–114

  32. López A, Lloret D, Serrat J, Villanueva J (2000) Multilocal creaseness based on the level-set extrinsic curvature. Comput Vis Image Understanding 77(1):111–144

    Article  Google Scholar 

  33. Schey HM (1973) “Div, grad, curl” and all that. Norton, New York

    MATH  Google Scholar 

  34. Bigun J, Granlund GH, Wiklund J (1991) Multidimensional orientation estimation with applications to texture analysis and optical flow. IEEE Trans PAMI 13(8):775–790

    Google Scholar 

  35. Jähne B (1993) Spatio-temporal image processing. Lect Notes Comput Sci 751:153–152

    Google Scholar 

  36. Niessen W, López A, Enk W, Roermund P-V, Romeny BH, Viergever M (1997) In vivo analysis of trabecular bone architecture. Lect Notes Comput Sci 1230:435–440

    Google Scholar 

  37. Zana F, Klein JC (1999) A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE Trans Med Imaging 18(5):419–428

    Article  PubMed  Google Scholar 

  38. Domingo J, Ayala G, Simó A, de Ves E, Martínez-Costa L, Marco P (1997) Irregular motion recovery in fluorescein angiograms. Pattern Recogn Lett 18(9):805–821

    Article  Google Scholar 

  39. Pinz A, Bernögger S, Datlinger P, Kruger A (1998) Mapping the human retina. IEEE Trans Med Imaging 17(4):606–619

    Article  PubMed  Google Scholar 

  40. den Elsen P, Maintz J, Pol DEJD, Viergever MA (1995) Automatic registration of CT and MR brain images using correlation of geometrical features. IEEE Trans Med Imaging 14(2):384–396

    Article  Google Scholar 

  41. Lewis JP (1995) Fast template matching. Vision Interf 120–123

  42. Ehrenfeld S, Littauer S (1964) Introduction to statistical method. McGraw-Hill, New York

Download references

Acknowledgments

This paper has been partially funded by the Xunta de Galicia through the grant contract PGIDT01PXI10502PR.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of A Coruña, 15071, A Coruña, Spain

    C. Mariño, M. G. Penedo & M. Penas

  2. Department of Electronics and Computer Science, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    M. J. Carreira

  3. Department of Physiology and Service of Ophthalmology, School of Medicine and Complejo Hospitalario Universitario of Santiago, 15782, Santiago de Compostela, Spain

    F. Gonzalez

Authors
  1. C. Mariño
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. G. Penedo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Penas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. J. Carreira
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. F. Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Mariño.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mariño, C., Penedo, M.G., Penas, M. et al. Personal authentication using digital retinal images. Pattern Anal Applic 9, 21–33 (2006). https://doi.org/10.1007/s10044-005-0022-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-005-0022-6

Keywords

Search

Navigation