Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Introduction to the Handbook of Iris Recognition Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

11. Robust and Secure Iris Recognition

verfasst von : Jaishanker K. Pillai, Vishal Patel, Rama Chellappa, Nalini Ratha

Erschienen in: Handbook of Iris Recognition

Verlag: Springer London

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Iris biometric entails using the patterns on the iris as a biometric for personal authentication. It has additional benefits over contact-based biometrics such as fingerprints and hand geometry. However, iris biometric often suffers from the following three challenges: ability to handle unconstrained acquisition, privacy enhancement without compromising security, and robust matching. This chapter discusses a unified framework based on sparse representations and random projections that can address these issues simultaneously. Furthermore, recognition from iris videos as well as generation of cancelable iris templates for enhancing the privacy and security is also discussed.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Introduction to the IrisCode Theory
Nächstes Kapitel Multispectral Iris Fusion and Cross-Spectrum Matching
Literatur
1.
2.
J.D. Blanchard, C. Cartis, J. Tanner, The restricted isometry property and \(\ell _{q}\) regularization: phase transition for sparse approximation. preprint
3.
R.M. Bolle, J.H. Connel, N.K. Ratha, Biometrics perils and patches. Pattern Recogn. 35(12), 2727–2738 (2002)CrossRefMATH
4.
K.W. Bowyer, P.J. Flynn, The ND-IRIS-0405 Iris Image Dataset
5.
K.W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110(2), 281–307 (2008)CrossRef
6.
E. Candes, J. Romberg, T. Tao, Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theor. 52(2), 489–509 (2006)
7.
E. Candes, J. Romberg, T. Tao, Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59, 1207–1223 (2006)MathSciNetCrossRefMATH
8.
Y. Chen, S.C. Dass, A.K. Jain, Localized iris image quality using 2-D wavelets, in Lecture Notes in Computer Science, vol. 3832 (Springer, 2006), pp. 373–381
9.
J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15, 1148–1161 (1993)CrossRef
10.
G.I. Davida, Y. Frankel, B.J. Matt, On enabling secure applications through off-line biometric identification, in IEEE Symposiam on Security and Privacy (1998), pp. 148–157
11.
D.L. Donoho, High-Dimensional centrally symmetric polytopes with neighborliness proportional to dimension. Discrete Comput. Geom. 35(4), 617–652 (2006)MathSciNetCrossRefMATH
12.
Y. Du, N.L. Thomas, E. Arslanturk, Multi-level iris video image thresholding, in IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, (CIB) (Mar. 2009), pp. 38–45
13.
F. Hao, R. Anderson, J. Daugman, Combining crypto with biometrics effectively. IEEE Trans. Comput. 55(9), 1081–1088 (2006)CrossRef
14.
K.P. Hollingsworth, K.W. Bowyer, P.J. Flynn, Image Averaging for Improved Iris Recognition. Lecture Notes in Computer Science, vol. 5558 (2009)
15.
A.K. Jain, K. Nandakumar, A. Nagar, Biometric template security. EURASIP J. Adv. Sig. Process. Spec. Issue Biometrics 2008(113), 1–17 (2008)CrossRef
16.
A. Juels, M. Wattenberg, A fuzzy commitment scheme, in ACM Conference on Computers and Communications Security (1999), pp. 28–36
17.
S. Kanade, D. Petrovska-Delacretaz, B. Dorizzi, Cancelable iris biometrics and using error correcting codes to reduce variability in biometric data, in Computer Vision and Pattern Recognition (2009)
18.
E. Krichen et al., Specific texture analysis for iris recognition. Lecture Notes in Computer Science, vol 3546 (Springer, Heidelberg, 2005), pp. 23–30
19.
C. Liu, M. Xie, Iris recognition based on DLDA, in International Conference on Pattern Recognition (2006), pp. 489–492
20.
L. Ma et al., Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)CrossRef
21.
L. Masek, P. Kovesi, MATLAB Source Code for a Biometric Identification System Based on Iris Patterns (2003)
22.
E.M. Newton, P.J. Phillips, Meta-analysis of third-party evaluations of iris recognition. IEEE Trans. Syst. Man Cybern. 39(1), 4–11 (2009)CrossRef
23.
P.J. Phillips, FRGC and ICE Workshop (2006)
24.
P. Phillips et al, Overview of the multiple biometrics grand challenge, in Advances in Biometrics (2009), pp. 705–714
25.
J.K. Pillai, V.M. Patel, R. Chellappa, Sparsity Inspired Selection and Recognition of Iris Images, in Third IEEE International Conference on Biometrics–Technology and Systems (2009)
26.
J.K. Pillai, V.M. Patel, R. Chellappa, Sparsity inspired selection and recognition of iris images, in IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems (BTAS) (Sept. 2009), pp. 1–6
27.
J.K. Pillai et al, Sectored random projections for cancelable iris biometrics, in International Conference on Acoustics, Speech, and Signal Processing (2009)
28.
N.K. Ratha, J.H. Connel, R.M. Bolle, Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001)CrossRef
29.
H. Rauhut, K. Schnass, P. Vandergheynst, Compressed sensing and redundant dictionaries. IEEE Trans. Inform.Theor. 54(5), 2210–2219 (2008)
30.
K. Roy, P. Bhattacharya, Iris recognition with support vector machines, in International Conference on Biometrics (2006), pp. 486–492
31.
N.A. Schmid, F. Nicolo, A method for selecting and ranking quality metrics for optimization of biometric recognition systems, in Computer Vision and Pattern Recognition Workshops (CVPR Workshops) (June 2009), pp. 126–133
32.
33.
A. Teoh, A. Goh, D. Ngo, Random multispace quantization as an analytic mechanism for biohashing of biometric and random identity inputs. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 1892–1901 (2006)CrossRef
34.
West Virginia University Iris Dataset
35.
J. Wright et al., Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRef
36.
J. Zuo, N. Ratha, J. Connell, Cancelable Iris Biometric, in Proceeding of International Conference on Pattern Recognition (2008), pp. 1–4
Metadaten
Titel
Robust and Secure Iris Recognition
verfasst von
Jaishanker K. Pillai
Vishal Patel
Rama Chellappa
Nalini Ratha
Copyright-Jahr
2016
Verlag
Springer London
Buch
Handbook of Iris Recognition

Print ISBN: 978-1-4471-6782-2

Electronic ISBN: 978-1-4471-6784-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-1-4471-6784-6_11