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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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
D. Roberts, Anatomy of the Eye. MD Support, (2013), [Online; accessed 5-July-2016].URL: http://www.mdsupport.org/information/99-2
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
Wikimedi Commons, File:Retina.svg. [Online; accessed 5-July-2016] https://commons.wikimedia.org/wiki/File:Retina.svg
Z. L. Stan (ed.), Encyclopedia of Biometrics (Springer, 2009). ISBN 978-0-387-73003-5
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
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)
P. Tower, The fundus oculi in monozygotic twins: report of six pairs of identical twins. A.M.A. Arch. Ophthalmol. 54, 225–239 (1955)
J. Daugman, How iris recognition works, in Proceedings of 2002 International Conference on Image Processing, vol. 1, (2002)
M. Tistarelli, S.Z. Li, R. Chellappa, Handbook of Remote Biometrics: For Surveillance and Security (Springer, 2009). ISBN 978-1-447-12670-6
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
R.B. Dubey, M. Abhimanyu, Iris localization using Daugman’s intero-differential operator. Int. J. Comp. Appl., 93, 6–12 (2014)
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
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
D.D. Zhang, Automated Biometrics: Technologies and Systems (Springer, 2013). ISBN 1461545196
A. Kumar, Biometric Security: Iris Recognition. [Online; accessed 10-August-2015] URL: http://www.slideshare.net/piyushmittalin/biometric-security-iris-recognition
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
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
H. Bouma, L. Baghuis, Hippus of the pupil: periods of slow oscillations of unknown origin. Vis. Res., 11 (11), 1345–1351 (1971)
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
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
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
IrisID, iCAM D1000, in An Eye Fundus Scanner. [Online; accessed 5-July-2016]. URL: http://www.irisid.com/productssolutions/hardwareproducts/icamd1000
Iritech, Inc., IriShield™ Series. [Online; accessed 5-July-2016]. URL: http://www.iritech.com/products/hardware/irishield%E2%84%A2-series#
J.P. Holmes, L.J. Wright, R.L. Maxwell, A Performance Evaluation of Biometric Identification Devices (Sandia National Laboratories, 1991). Technical Report SAND91-0276
C. Simon, I. Goldstein, A new scientific method of identification. N. Y. State J. Med. 35(18), 901–906
R.B. Hill, Biometrics: Retina Identification: Personal Identification in Networked Society (Springer, 2006). ISBN 978-0-387-28539-9
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
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
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
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
Trans Pacific (GNF) International, Inc., EyeKey System. [Online; accessed 5-July-2016]. URL: http://www.tpi-gnf.com/eky1.htm
Retinal Technologies, A Handheld Scanner. [Online; accessed 5-July-2016]. URL: http://biometrics.mainguet.org/types/eye_retinal.htm
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
A.K. Jain, A.A. Ross, K. Nandakumar, Handbook of Multibiometrics (Springer, New York, 2006). ISBN 978-038-7331-232
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
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
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
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
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
K. Nandakumar, Multibiometric Systems: Fusion Strategies and Template Security (ProQuest, 2008). ISBN: 978-0-549-61747-1
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
N. Radha, A. Kavitha, Rank level fusion using fingerprint and iris biometrics, in Indian Journal of Computer Science and Engineering, (2012), pp. 917–923
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
D.F. Muller, G.L. Heacock, D.B. Usher, Method and System for Generating a Combined Retina/Iris Pattern Biometric, US Patent 7248720, (2007)
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
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
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
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
J.G. Daugman, Biometric Personal Identification System Based on Iris Analysis. US Patent 5291560, (1994)
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)
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
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
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
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
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
C. Köse, C. İkibaş, A personal identification system using retinal vasculature in retinal fundus images. Expert Syst. Appl. 38(11), 13670–13681 (2011)
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
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
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
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
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
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
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
J. Parker, Algorithms for Image Processing and Computer Vision (Wiley Computer Publishing, New York, 1997). ISBN 04-711-4056-2
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
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)
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
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
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
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
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)
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
B. Jähne, Digital Image Processing Concepts, Algorithms, and Scientific Applications (Springer, Berlin Heidelberg, 1997). ISBN 978-3-662-03479-8
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
J. Parker, Algorithms for Image Processing and Computer Vision (John Wiley & Sons, 1996). isbn:0471140562
P.S. Heckbert, Graphics Gems IV, Graphic Gems Series (AP Professional, 1994). ISBN 0-12-336155-9
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
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
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)
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
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
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
H. Rai, A. Yadav, Iris recognition using combined support vector machine and hamming distance approach. Exp. Syst. Appl. 41(2), 588–593 (2014)
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
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
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)
M. Yanoff, Ophthalmology, 3rd edn. (Mosby Elsevier, 2009). ISBN 978-0-323-04332-8
Mayo Clinic, Indirect Ophthalmoscopy. [Online; accessed 5-July-2016]. URL: http://www.mayoclinic.org/tests-procedures/eye-exam/multimedia/indirect-ophthalmoscopy/img-20006175
S.E. Sherman, History of Ophthalmology: The History of the Ophthalmoscope, vol 2 (Springer, 1989), pp. 221–228 ISBN 978-0-7923-0273-5
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
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
A.P. Schachat, P. Wilkinson Ch, D.R. Hinton, P. Wilkinson, Retina, 4th edn. (Mosby Elsevier, 2005). ISBN 978-0-323-04323-6
L. Poretsky (ed.), Principles of Diabetes Mellitus, 2nd edn. (Springer, 2010). ISBN 978-0-387-09840-1
W. Gloria, Diabetic Retinopathy: The Essentials. LWW; 1 Har/Psc edition, (2010.), ISBN 1605476625
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
W. Lihteh, Ophthalmologic Manifestations of Toxoplasmosis. [Online; accessed 5-July-2016]. URL: http://emedicine.medscape.com/article/2044905-overview
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
C. Tisse, L. Martin, L. Torres, M. Robert, Person identification technique using human iris recognition, in Proceedings of ICVI 2002, (2002), pp. 294–299
H.E. Lahn, Iridology: The Diagnosis from the Eye (Kessinger Publishing, 2010). ISBN 978-1162622729
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
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
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
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this chapter
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)