Skip to main content
SpringerLink
Log in
Book cover

Biometric-Based Physical and Cybersecurity Systems pp 37–102Cite as

Recognition-Based on Eye Biometrics: Iris and Retina

  • Chapter
  • First Online:

Abstract

The chapter describes biometric identification based on inner eye organs – iris and retina. These methods are very precise and are used in areas with highest security requirements. Eye attributes that are being scanned and used for identification are unique for each individual, and the probability of two same identifiers is many times lower, for example, in comparison with fingerprints recognition.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wikimedia Commons, Eyesection.svg. [Online; accessed 5-July-2016]. URL: https://upload.wikimedia.org/wikipedia/commons/thumb/f/f5/Eyesection.svg/2000px-Eyesection.svg.png

  2. D. Roberts, Anatomy of the Eye. MD Support, (2013), [Online; accessed 5-July-2016].URL: http://www.mdsupport.org/information/99-2

  3. K. Franklin, P. Muir, T. Scott, L. Wilcocks, P. Yates, Introduction to Biological Physics for the Health and Life Sciences (Wiley Blackwell, 2010). ISBN 978-0470665930

    Google Scholar 

  4. Wikimedi Commons, File:Retina.svg. [Online; accessed 5-July-2016] https://commons.wikimedia.org/wiki/File:Retina.svg

  5. Z. L. Stan (ed.), Encyclopedia of Biometrics (Springer, 2009). ISBN 978-0-387-73003-5

    Google Scholar 

  6. L. Yang, Iris/Retina Biometrics. CPSC 4600@UTC/CSE, [Online; accessed 5-July-2016]. URL: http://web2.utc.edu/~djy471/documents/b6.1.IRIS-Retina-utc.ppt

  7. R.P. Moreno, A. Gonzaga, Features vector for personal identification based on Iris texture, in Proceedings of the Irish Machine Vision and Image Processing Conference, (Dublin, 2004)

    Google Scholar 

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

    Article  Google Scholar 

  9. J. Daugman, How iris recognition works, in Proceedings of 2002 International Conference on Image Processing, vol. 1, (2002)

    Google Scholar 

  10. M. Tistarelli, S.Z. Li, R. Chellappa, Handbook of Remote Biometrics: For Surveillance and Security (Springer, 2009). ISBN 978-1-447-12670-6

    Google Scholar 

  11. J. Daugman, How iris recognition works, in IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, (2004), [Online; accessed 5-July-2016]: URL: https://www.cl.cam.ac.uk/~jgd1000/csvt.pdf

    Article  Google Scholar 

  12. R.B. Dubey, M. Abhimanyu, Iris localization using Daugman’s intero-differential operator. Int. J. Comp. Appl., 93, 6–12 (2014)

    Google Scholar 

  13. M. Adam, F. Rossant, F. Amiel, B. Mikovicova, T. Ea, Reliable eyelid localization for iris recognition, in Conference: Advanced Concepts for Intelligent Vision Systems, ACIVS 2008, (2008). https://doi.org/10.1007/978-3-540-88458-3_96

    Chapter  Google Scholar 

  14. T. Johar, P. Kaushik, Iris segmentation and normalization using Daugman’s rubber sheet model. Int. J. Sci. Tech. Adv. 1(3) (2015). ISSN: 2454-1532

    Google Scholar 

  15. D.D. Zhang, Automated Biometrics: Technologies and Systems (Springer, 2013). ISBN 1461545196

    Google Scholar 

  16. A. Kumar, Biometric Security: Iris Recognition. [Online; accessed 10-August-2015] URL: http://www.slideshare.net/piyushmittalin/biometric-security-iris-recognition

  17. J. Daugman, Results from 200 Billion Iris Cross-Comparisons. University of Cambridge, Technical report, (2005), ISSN 1476–2986. URL: https://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-635.pdf

  18. P. Khav, Iris Recognition Technology for Improved Authentication, (SANS Institute, SANS Security Essentials (GSEC) Practical Assignment, 2002), [Online; accessed 5-July-2016]. URL: https://www.sans.org/reading-room/whitepapers/authentication/iris-recognition-technology-improved-authentication-132

  19. H. Bouma, L. Baghuis, Hippus of the pupil: periods of slow oscillations of unknown origin. Vis. Res., 11 (11), 1345–1351 (1971)

    Article  Google Scholar 

  20. I.D. Iris, El Salvador Sugar Mill Uses Iris Recognition for Time and Attendance. [Online; accessed 5-July-2016]. URL: http://www.irisid.com/el-salvador-sugar-mill-uses-iris-recognition-for-time-and-attendance

  21. Panasonic Corporation, Iris Reader Access Control, BM-ET200. [Online; accessed 5-July-2016]. URL: ftp://ftp.panasonic.com/pub/panasonic/cctv/BidSpecs/BM-ET200.rtf

  22. ID Travel AG, Biometric Systems for Secure and Rapid Access: IrisAccess 4000. [Online; accessed 5-July-2016]. http://www.id-travel.ch/Downloads/FS_IrisAccess_en.pdf

  23. IrisID, iCAM D1000, in An Eye Fundus Scanner. [Online; accessed 5-July-2016]. URL: http://www.irisid.com/productssolutions/hardwareproducts/icamd1000

  24. Iritech, Inc., IriShield™ Series. [Online; accessed 5-July-2016]. URL: http://www.iritech.com/products/hardware/irishield%E2%84%A2-series#

  25. J.P. Holmes, L.J. Wright, R.L. Maxwell, A Performance Evaluation of Biometric Identification Devices (Sandia National Laboratories, 1991). Technical Report SAND91-0276

    Google Scholar 

  26. C. Simon, I. Goldstein, A new scientific method of identification. N. Y. State J. Med. 35(18), 901–906

    Google Scholar 

  27. R.B. Hill, Biometrics: Retina Identification: Personal Identification in Networked Society (Springer, 2006). ISBN 978-0-387-28539-9

    Google Scholar 

  28. J.P. Holme, L.J. Wright, R.L. Maswell, A Performance Evaluation of Biometric Identification Devices. Sandia report, SAND91–0276, (1991), [Online; accessed 5-July-2016]. URL: http://prod.sandia.gov/techlib/access-control.cgi/1991/910276.pdf

  29. J. Farmer, Stop All Federal Abuses Now! S.A.F.A.N. Internet Newsletter, No. 264, (1997), [Online; accessed 5-July-2016]. URL: http://www.iahushua.com/WOI/illinois.htm

  30. EyeDentify Inc., The Ultimate in Positive Identification, EyeDentification system 7.5. Leaflet, (1985), [Online; accessed 5-July-2016]. URL: http://simson.net/ref/biometrics/Biometrics/1985.Eyedentify.System7.5.pdf

  31. Rayco Security Loss Prevention Systems, Inc., Retina Verification, ICAM 2001, EyeDentify Retina Biometric Reader. [Online; accessed 5-July-2016]. URL: http://www.raycosecurity.com/biometrics/EyeDentify.html

  32. Trans Pacific (GNF) International, Inc., EyeKey System. [Online; accessed 5-July-2016]. URL: http://www.tpi-gnf.com/eky1.htm

  33. Retinal Technologies, A Handheld Scanner. [Online; accessed 5-July-2016]. URL: http://biometrics.mainguet.org/types/eye_retinal.htm

  34. C. Holmes, S. Walmsley, Biometric Security in Today’s Market: An Introduction to Fingerprint, Retinal, and Iris Scanning Technologies. COP4910 – Frontiers in Information Technology, 2005, [Online; accessed 5-July-2016]. URL: http://pegasus.cc.ucf.edu/~cholmes/homepage/Biometrics.doc

  35. A.K. Jain, A.A. Ross, K. Nandakumar, Handbook of Multibiometrics (Springer, New York, 2006). ISBN 978-038-7331-232

    Google Scholar 

  36. L. Hong, Y. Wan, A.K. Jain, Fingerprint image enhancement: algorithms and performance evaluation, in IEEE Transactions on Pattern Analysis and Machine Intelligence, (1998), pp. 777–789

    Google Scholar 

  37. T. Matsumoto, H. Matsumoto, K. Yamada, S. Hoshino, R.L. Renesse, Impact of artificial "gummy" fingers on fingerprint systems, in Optical Security and Counterfeit Deterrence Techniques, (2002), pp. 275–289. https://doi.org/10.1117/12.462719

    Chapter  Google Scholar 

  38. Techbiometric, Advantages of Multi-biometric Systems Over Unibiometric Systems. [Online; accessed 5-July-2016]. URL: http://techbiometric.com/articles/advantages-of-multi-biometric-systems-over-unibiometric-systems

  39. M. Faundez-Zanuy, L. O'Gorman, A.K. Jain, N.K. Ratha, Data fusion in biometrics, in IEEE Aerospace and Electronic Systems Magazine, (2005), pp. 34–38. https://doi.org/10.1109/MAES.2005.1396793

    Chapter  Google Scholar 

  40. S. Paunovic, I. Jerinić, D. Starčević, Methods for biometric data connection in multimodal systems, in Proceedings of the XIV International Symposium SYMORG 2014: New Business Models and Sustainable Competitiveness, (2014), pp. 900–906 ISBN 978-8-676-80295-1

    Google Scholar 

  41. K. Nandakumar, Multibiometric Systems: Fusion Strategies and Template Security (ProQuest, 2008). ISBN: 978-0-549-61747-1

    Google Scholar 

  42. A.K. Jain, B. Chandrasekaran, N.K. Ratha, Dimensionality and sample size considerations in pattern recognition practice, in Handbook of Statistics, (1982), p. 835. https://doi.org/10.1016/S0169-7161(82)02042-2

    Chapter  Google Scholar 

  43. N. Radha, A. Kavitha, Rank level fusion using fingerprint and iris biometrics, in Indian Journal of Computer Science and Engineering, (2012), pp. 917–923

    Google Scholar 

  44. L. Latha, S. Thangasamy, A robust person authentication system based on score level fusion of left and right irises and retinal features, in Proceedings of the International Conference and Exhibition on Biometrics Technology, vol. 2, (2010), pp. 111–120. https://doi.org/10.1016/j.procs.2010.11.014

    Chapter  Google Scholar 

  45. D.F. Muller, G.L. Heacock, D.B. Usher, Method and System for Generating a Combined Retina/Iris Pattern Biometric, US Patent 7248720, (2007)

    Google Scholar 

  46. D. Usher, Y. Tosa, M. Friedman, Ocular biometrics: simultaneous capture and analysis of the retina and iris, in Advances in Biometrics: Sensors, Algorithms and Systems, (Springer, 2008), pp. 133–155 ISBN: 1846289203

    Google Scholar 

  47. Find Biometrics, Retica Systems Inc. Announces the World’s First Iris-Retina Biometric System. 2006, [Online; accessed 5-July-2016]. URL: http://findbiometrics.com/retica-systems-inc-announces-the-worlds-first-iris-retina-biometric-system

  48. PR Newswire, Retica Systems Inc. Announces the World's First Iris-Retina Biometric System. News 2006, [Online; accessed 5-July-2016]. URL: http://www.prnewswire.com/news-releases/retica-systems-inc-announces-the-worlds-first-iris-retina-biometric-system-55935287.html

  49. A. Jóźwik, D. Siedlecki, M. Zając, Analysis of Purkinje images as an effective method for estimation of intraocular lens implant location in the eyeball. Optik Int. J. Light Electron Opt. 125(20), 6021–6025 (2014). https://doi.org/10.1016/j.ijleo.2014.06.130

    Article  Google Scholar 

  50. J.G. Daugman, Biometric Personal Identification System Based on Iris Analysis. US Patent 5291560, (1994)

    Google Scholar 

  51. R.P. Wildes, J.C. Asmuth, K.J. Hanna, S.C. Hsu, R.J. Kolczynski, J.R. Matey, S.E. McBride, Automated, Non-invasive Iris Recognition System and Method. US Patents 5572596 and 5751836, (1996)

    Google Scholar 

  52. M. Barbosa, A.C. James, Joint iris boundary detection and fit: a real-time method for accurate pupil tracking. Biomed. Opt. Express 5(8), 2458–2470 (2014). https://doi.org/10.1364/BOE.5.002458

    Article  Google Scholar 

  53. I.A. Saad, L.E. George, Robust and fast iris localization using contrast stretching and leading edge detection. Int. J. Emerg. Trends Technol. Comput. Sci. 3, 61–67 (2014). ISSN 2278-6856

    Google Scholar 

  54. S. Qamber, Z. Waheed, M.U. Akram, Personal identification system based on vascular pattern of human retina, in Cairo International Biomedical Engineering Conference, 2012, vol. 2012, pp. 64–677 ISBN 978-1-4673-2800-5

    Google Scholar 

  55. H. Oinonen, H. Forsvik, P. Ruusuvuori, O. Yli-Harja, V. Voipio, H. Huttunen, Identity verification based on vessel matching from fundus images, in Proceedings of IEEE International Conference on Image Processing, (2010), pp. 4089–4092 ISBN 978-1-4244-7993-1, ISSN 1522-4880

    Google Scholar 

  56. C. Mariño, M.G. Penedo, M. Penas, M.J. Carreira, F. Gonzalez, Personal authentication using digital retinal images. Springer Pattern Anal. Appl 9(1), 21–33 (2006). ISSN 1433-7541

    Article  MathSciNet  Google Scholar 

  57. C. Köse, C. İkibaş, A personal identification system using retinal vasculature in retinal fundus images. Expert Syst. Appl. 38(11), 13670–13681 (2011)

    Google Scholar 

  58. W. Barkhoda, F. Akhlaqian, M. Amiri, M. Nouroozzadeh, Retina identification based on the pattern of blood vessels using fuzzy logic, in EURASIP Journal of Advances in Signal Processing, (2011), pp. 113–121 ISSN: 1687-6180

    Google Scholar 

  59. H. Borgen, P. Bours, Wolthusen S. D., Visible-spectrum biometric retina recognition, in Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, (2008), pp. 1056–1062 ISBN 978-0-7695-3278-3

    Google Scholar 

  60. G.V. Saradhi, S. Balasubramanian, V. Chandrasekaran, Performance enhancement of optic disc boundary detection using active contours via improved homogenization of optic disc region, in International Conference on Information and Automation, (ICIA, 2006), pp. 264–269 ISSN 2151-1802

    Google Scholar 

  61. P.C. Siddalingaswamy, G.K. Prabhu, Automated detection of anatomical structures in retinal images. Int. Conf. Comput. Intell. Multimed. Appl. 3(10), 164–168 (2007). ISBN 0-7695-3050-8

    Google Scholar 

  62. S. Tamura, Y. Okamoto, K. Yanashima, Zero-crossing interval correction in tracing eye-fundus blood vessels. Pattern Recogn. 3, 227–233 (1988). https://doi.org/10.1016/0031-3203(88)90057-x

    Article  Google Scholar 

  63. A. Pinz, S. Bernogge, P. Datlinger, et al., Mapping the human retina, in IEEE Transactions on Medical Imaging, No. 4, (1998), pp. 606–619. https://doi.org/10.1109/42.730405

    Chapter  Google Scholar 

  64. D.W.K. Wong, J. Liu, N.M. Tan, et al., Automatic detection of the macula in retinal fundus images using seeded mode tracking approach, in IEEE Engineering in Medicine and Biology Society, Institute of Electrical & Electronics Engineers (IEEE), (2012). https://doi.org/10.1109/embc.2012.6347103

    Chapter  Google Scholar 

  65. J. Parker, Algorithms for Image Processing and Computer Vision (Wiley Computer Publishing, New York, 1997). ISBN 04-711-4056-2

    Google Scholar 

  66. C. Sinthanayothin, J.F. Boyce, H.L. Cook, et al., Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999). 10.1136/bjo.83.8.902

    Article  Google Scholar 

  67. S. Umbaugh, Digital Image Processing and Analysis: Human and Computer Vision Applications with CVIPtools (CRC Press., ISBN 978-1-4398-0205-2, Boca Raton, FL, 2011)

    Google Scholar 

  68. T. Johar, P. Kaushik, Iris segmentation and normalization using Daugman’s rubber sheet model. Int. J. Sci. Tech. Adv. 1(3) (2015). ISSN: 2454-1532

    Google Scholar 

  69. G. Kavitha, S. Ramakrishnan, Identification and analysis of macula in retinal images using Ant Colony Optimization based hybrid method, in 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), Institute of Electrical & Electronics Engineers (IEEE), (2009). https://doi.org/10.1109/nabic.2009.5393783

    Chapter  Google Scholar 

  70. M. Mubbashar, A. Usman, M.U. Akram, Automated system for macula detection in digital retinal images, in IEEE International Conference on Information and Communication Technologies, (2011). https://doi.org/10.1109/icict.2011.5983555

    Chapter  Google Scholar 

  71. D.W.K. Wong, J. Liu, N.M. Tan, et al., Automatic detection of the macula in retinal fundus images using seeded mode tracking approach, in IEEE Engineering in Medicine and Biology Society, Institute of Electrical & Electronics Engineers (IEEE), (2012). https://doi.org/10.1109/embc.2012.6347103

    Chapter  Google Scholar 

  72. P. Verlinde, G. Chollet, Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application, in Second International Conference on Audio and Video-Based Biometric Person Authentication, (2003)

    Google Scholar 

  73. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, in IEEE Transactions on Medical Imaging, No. 3, (1989), pp. 263–269 ISSN 0278-0062

    Google Scholar 

  74. B. Jähne, Digital Image Processing Concepts, Algorithms, and Scientific Applications (Springer, Berlin Heidelberg, 1997). ISBN 978-3-662-03479-8

    MATH  Google Scholar 

  75. A. Aquino, M.E. Gegúndez, D. Marín, Automated optic disc detection in retinal images of patients with diabetic retinopathy and risk of macular edema, in World Academy of Science, Engineering and Technology, No. 60, (2009), pp. 87–92

    Google Scholar 

  76. J. Parker, Algorithms for Image Processing and Computer Vision (John Wiley & Sons, 1996). isbn:0471140562

    Google Scholar 

  77. P.S. Heckbert, Graphics Gems IV, Graphic Gems Series (AP Professional, 1994). ISBN 0-12-336155-9

    Chapter  Google Scholar 

  78. N. Dede, Implementation of Thinning Algorithm in OpenCV. OpenCV Code, [Online; accessed 5-July-2016]. URL: http://opencv-code.com/quick-tips/implementation-of-thinning-algorithm-in-opencv

  79. J.G. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993). ISSN 0162-8828

    Article  Google Scholar 

  80. L. Ma, T. Tan, Y. Wang, D. Zhang, Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)

    Article  Google Scholar 

  81. D.M. Monro, S. Rakshit, D. Zhang, DCT-based iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 586–595 (2007). ISSN 0162-8828

    Article  Google Scholar 

  82. S. Sun, S. Yang, L. Zhao, Non-cooperative bovine iris recognition via SIFT. Neurocomputing 120, 310–317 (2013). https://doi.org/10.1016/j.neucom.2012.08.068

    Article  Google Scholar 

  83. H. Mehrotra, P.K. Sa, B. Majhi, Fast segmentation and adaptive SURF descriptor for iris recognition. Math. Comput. Modell., Elsevier 58(1–2, 132), –146 (2013). ISSN: 0895-7177

    Article  Google Scholar 

  84. H. Rai, A. Yadav, Iris recognition using combined support vector machine and hamming distance approach. Exp. Syst. Appl. 41(2), 588–593 (2014)

    Article  Google Scholar 

  85. T. Ojala, T. Pietikäinen, D. Harwood, Performance evaluation of texture measures with classification based on Kullback discrimination of distributions, in Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, (1994), pp. 582–585 ISBN 0-8186-6265-4

    Chapter  Google Scholar 

  86. M.Y. Shams, M.Z. Rashad, O. Nomir, M.Z. El-Awady, Iris recognition based on LBP and combined LVQ classifier. Int. J. Comput. Sci. Inf. Technol. 3(5) (2011). https://doi.org/10.5121/ijcsit.2011.3506

    Article  Google Scholar 

  87. J. Macek, Klasifikace a rozpoznávání patologických nálezů v obrazech sítnice oka (Classification and Recognition of Pathologic Foundings in Eye Retina Images). Master's thesis, Faculty of Information Technology, Brno University of Technology, (2015)

    Google Scholar 

  88. M. Yanoff, Ophthalmology, 3rd edn. (Mosby Elsevier, 2009). ISBN 978-0-323-04332-8

    Google Scholar 

  89. Mayo Clinic, Indirect Ophthalmoscopy. [Online; accessed 5-July-2016]. URL: http://www.mayoclinic.org/tests-procedures/eye-exam/multimedia/indirect-ophthalmoscopy/img-20006175

  90. S.E. Sherman, History of Ophthalmology: The History of the Ophthalmoscope, vol 2 (Springer, 1989), pp. 221–228 ISBN 978-0-7923-0273-5

    Google Scholar 

  91. R.L. Wiggins, K.D. Vaughan, G.B. Friedmann, Holography using a fundus camera. Appl. Opt. (1), 179–181 (1972). https://doi.org/10.1364/AO.11.000179

    Article  Google Scholar 

  92. J. Orellana, A.H. Friedman, Clinico-Pathological Atlas of Congenital Fundus Disorders: Best’s Disease (Springer, 1993), pp. 147–150., ISBN 978-1-4613-9322-1

    Google Scholar 

  93. A.P. Schachat, P. Wilkinson Ch, D.R. Hinton, P. Wilkinson, Retina, 4th edn. (Mosby Elsevier, 2005). ISBN 978-0-323-04323-6

    Google Scholar 

  94. L. Poretsky (ed.), Principles of Diabetes Mellitus, 2nd edn. (Springer, 2010). ISBN 978-0-387-09840-1

    Google Scholar 

  95. W. Gloria, Diabetic Retinopathy: The Essentials. LWW; 1 Har/Psc edition, (2010.), ISBN 1605476625

    Google Scholar 

  96. American Optometric Association, Diabetic eye disease, in Diabetic Eye Disease, (2009), [Online; accessed 5-July-2016], URL: http://www.slideshare.net/MedicineAndHealth14/diabetic-eye-disease

  97. W. Lihteh, Ophthalmologic Manifestations of Toxoplasmosis. [Online; accessed 5-July-2016]. URL: http://emedicine.medscape.com/article/2044905-overview

  98. J.D. Camet, H. Talabani, E. Delair, F. Leslé, H. Yera, A.P. Brézin, Toxoplasmosis – Recent Advances: Risk Factors, Pathogenesis and Diagnosis of Ocular Toxoplasmosis (Intech, 2012). ISBN 978-953-51-0746-0

    Google Scholar 

  99. C. Tisse, L. Martin, L. Torres, M. Robert, Person identification technique using human iris recognition, in Proceedings of ICVI 2002, (2002), pp. 294–299

    Google Scholar 

  100. H.E. Lahn, Iridology: The Diagnosis from the Eye (Kessinger Publishing, 2010). ISBN 978-1162622729

    Google Scholar 

  101. M.U. Akram, S. Khalid, S.A. Khan, Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn. 46(1), 107–116 (2013). https://doi.org/10.1016/j.patcog.2012.07.002.s

    Article  Google Scholar 

  102. S. Qamber, Z. Waheed, M.U. Akram, Personal identification system based on vascular pattern of human retina, in Cairo International Biomedical Engineering Conference, 2012, (2012), pp. 64–677 ISBN 978-1-4673-2800-5

    Chapter  Google Scholar 

Download references

Acknowledgment

This work was supported by the Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project IT4Innovations excellence in science – LQ1602.

Author information

Authors and Affiliations

  1. Brno University of Technology, Centre of Excellence IT4Innovations, Brno, 62166, Czech Republic

    Josef Hájek & Martin Drahanský

Authors
  1. Josef Hájek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Martin Drahanský
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Martin Drahanský .

Editor information

Editors and Affiliations

  1. ECE Department, Nazrabayev University, Astana, Kazakhstan

    Mohammad S. Obaidat

  2. Department of Electrical and Computer Engineering, University of Victoria – UVIC, Victoria, BC, Canada

    Issa Traore

  3. Department of Computer Science, Ryerson University, Toronto, ON, Canada

    Isaac Woungang

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Hájek, J., Drahanský, M. (2019). Recognition-Based on Eye Biometrics: Iris and Retina. In: Obaidat, M., Traore, I., Woungang, I. (eds) Biometric-Based Physical and Cybersecurity Systems. Springer, Cham. https://doi.org/10.1007/978-3-319-98734-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-98734-7_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98733-0

  • Online ISBN: 978-3-319-98734-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation