Design Decisions for an Iris Recognition SDK

  • Christian Rathgeb
  • Andreas Uhl
  • Peter Wild
  • Heinz Hofbauer
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

Open-source software development kits are vital to (iris) biometric research in order to achieve comparability and reproducibility of research results. In addition, for further advances in the field of iris biometrics the community needs to be provided with state-of-the-art reference systems, which serve as adequate starting point for new research. This chapter provides a summary of relevant design decisions for software modules constituting an iris recognition system. The proposal of general criteria and adequate concepts is complemented by a detailed description of how according design decisions are implemented in the University of Salzburg Iris Toolkit, an open-source iris recognition software which contains diverse algorithms for iris segmentation, feature extraction, and comparison. Building upon a file-based processing chain, the provided open-source software is designed to support rapid prototyping as well as integration in existing frameworks achieving enhanced usability and extensibility. In order to underline the competitiveness of the presented iris recognition software, experimental evaluations of segmentation and feature extraction algorithms are carried out on a publicly available iris database and compared to a commercial product.

Notes

Acknowledgments

This work was partially supported by the European FP7 FIDELITY project (SEC-2011-284862), the Center for Advanced Security Research Darmstadt (CASED) and the Austrian Science Fund, project no. P26630.

References

  1. 1.
    T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(120, 2037–2041 (2006)Google Scholar
  2. 2.
    T. Ahonen et al., Recognition of blurred faces using local phase quantization, in International Conference on Pattern Recognition (2008), pp. 1–4Google Scholar
  3. 3.
    F. Alonso-Fernandez, J. Bigun, Quality factors affecting iris segmentation and matching, in Proceedings of International Conference on Biometrics (ICB’13) (2013)Google Scholar
  4. 4.
    F. Alonso-Fernandez et al., Iris recognition based on SIFT features, in International Conference on Biometrics, Identity and Security (BIdS) (2009), pp. 1–8Google Scholar
  5. 5.
    H. Bay et al., Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)CrossRefGoogle Scholar
  6. 6.
    BioSecure project. Accessed June 2015Google Scholar
  7. 7.
    K.W. Bowyer, K. Hollingsworth, P.J. Flynn, Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110(2), 281–307 (2007)CrossRefGoogle Scholar
  8. 8.
    M. Boyd et al., Project Iris: free software for iris recognition (2010). Accessed June 2015Google Scholar
  9. 9.
    J. Cauchie, V. Fiolet, D. Villers, Optimization of an Hough transform algorithm for the search of a center. Pattern Recogn. 41(2), 567–574 (2008)CrossRefMATHGoogle Scholar
  10. 10.
    J. Daugman, High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)CrossRefGoogle Scholar
  11. 11.
    J. Daugman, How iris recognition works. IEEE Trans. Circ. Syst. Video Technol. 14(1), 21–30 (2004)CrossRefGoogle Scholar
  12. 12.
    Unique Identification Authority of India, Aadhaar, http://uidai.gov.in/. Accessed June 2015
  13. 13.
    Face Recognition Homepage. Source Codes. Accessed June 2015Google Scholar
  14. 14.
    J. Fierrez et al., BioSec baseline corpus: a multimodal biometric database. Pattern Recogn. 40(4), 1389–1392 (2007)CrossRefMATHGoogle Scholar
  15. 15.
    J. Hämmerle-Uhl, E. Pschernig, A. Uhl, Cancelable iris biometrics using block re-mapping and image warping, in Proceedings of 12th International Information Security Conference, ed. by P. Samarati et al. vol. 5735. LNCS. (Springer, 2009), pp. 135–142Google Scholar
  16. 16.
    R. Hentati et al., Measuring the quality of IRIS segmentation for Improved IRIS recognition performance, in 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS) (2012), pp. 110–117Google Scholar
  17. 17.
    R. Hentati, M. Abid, B. Dorizzi, Software implementation of the OSIRIS iris recognition algorithm in FPGA, in 2011 International Conference on Microelectronics (ICM) (2011), pp. 1–5Google Scholar
  18. 18.
    H. Hofbauer et al., A ground truth for iris segmentation, in 2014 22nd International Conference on Pattern Recognition (ICPR) (2014), pp. 527–532Google Scholar
  19. 19.
    Institute of Automation, Chinese Academy of Sciences (CASIA). Biometrics Ideal Test. Accessed June 2015Google Scholar
  20. 20.
    J. Kannala, E. Rahtu, BSIF: binarized statistical image features, in IEEE International Conference on Pattern Recognition (2012), pp. 1363–1366Google Scholar
  21. 21.
    K.P.H. Kevin, W. Bowyer, P.J. Flynn, A survey of iris biometrics research: 2008–2010, in Handbook of Iris Recognition (Springer, 2013), pp. 15–54Google Scholar
  22. 22.
    J.-G. Ko et al., A novel and efficient feature extraction method for iris recognition. ETRI J. 29(3), 399–401 (2007)CrossRefGoogle Scholar
  23. 23.
    E. Krichen et al., A biometric reference system for iris. OSIRIS version 4.1 (2013). Accessed June 2015Google Scholar
  24. 24.
    A. Kumar, A. Passi, Comparison and combination of iris matchers for reliable personal identification. Proc. CVPR 2008, 21–27 (2008)MATHGoogle Scholar
  25. 25.
    A. Kumar, A. Passi, Comparison and combination of iris matchers for reliable personal authentication. Pattern Recogn. 43(3), 1016–1026 (2010)CrossRefMATHGoogle Scholar
  26. 26.
    Y. Lee et al., VASIR: an open-source research platform for advanced iris recognition technologies. J. Res. NIST 118, 218–259 (2013). Accessed June 2015Google Scholar
  27. 27.
    D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)CrossRefGoogle Scholar
  28. 28.
    L. Ma et al., Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)CrossRefGoogle Scholar
  29. 29.
    L. Masek, Recognition of human iris patterns for biometric identification, MA Thesis. University of Western Australia (2003)Google Scholar
  30. 30.
    D. Monro, S. Rakshit, D. Zhang, Iris challenge evaluation (2006)Google Scholar
  31. 31.
    D.M. Monro, S. Rakshit, D. Zhang, DCT-based iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 586–595 (2007)CrossRefGoogle Scholar
  32. 32.
    J.C. Monteiro et al., MobBIO 2013: 1st biometric recognition with portable devices competition (2013)Google Scholar
  33. 33.
    T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  34. 34.
    U. Park, A. Ross, A. Jain, Periocular biometrics in the visible spectrum: a feasibility study, in IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, 2009. BTAS ’09 (2009), pp. 1–6Google Scholar
  35. 35.
    U. Park et al., Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. 6(1), 96–106 (2011)CrossRefGoogle Scholar
  36. 36.
    P. Phillips, K. Bowyer, P.J. Flynn, Comments on the CASIA version 1.0 iris data set. IEEE Trans. Pattern Anal. Mach. Intell. 29(10), 1869–1870 (2007)CrossRefGoogle Scholar
  37. 37.
    P.J. Phillips et al., FRVT 2006 and ICE 2006 large-scale experimental results. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 1–1 (2010)CrossRefGoogle Scholar
  38. 38.
    H. Proenca et al., The UBIRIS.v2: a database of visible wavelength images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010)CrossRefGoogle Scholar
  39. 39.
    H. Proença, L. Alexandre, Toward covert iris biometric recognition: experimental results from the NICE contests. IEEE Trans. Inf. Forensics Secur. 7(2), 798–808 (2012)CrossRefGoogle Scholar
  40. 40.
    R. Rakvic et al., Parallelizing iris recognition. IEEE Trans. Inf. Forensics Secur. 4(4), 812–823 (2009)CrossRefGoogle Scholar
  41. 41.
    C. Rathgeb, A. Uhl, Secure iris recognition based on local intensity variations, in Proceedings of the 7th International Conference on Image Analysis and Recognition—Volume Part II. ICIAR’10 (2010), pp. 266–275Google Scholar
  42. 42.
    C. Rathgeb, A. Uhl, P. Wild, Iris Recognition: From Segmentation to Template Security. Advances in Information Security, vol. 59 (Springer, 2013)Google Scholar
  43. 43.
    C. Rathgeb, A. Uhl, Context-based biometric key generation for Iris. IET Comput. Vis. 5(6), 389–397 (2011)CrossRefGoogle Scholar
  44. 44.
    E.S. Raymond, The Cathedral and the Bazaar, ed. by T. O’Reilly. 1st edn. (O’Reilly & Associates Inc., 1999)Google Scholar
  45. 45.
    A. Ross et al., Matching highly non-ideal ocular images: an information fusion approach, in Proceedings of 5th International Conference on Biometrics (2012)Google Scholar
  46. 46.
    A. Ross, Iris recognition: the path forward. IEEE Comput. 43(2), 30–35 (2010)Google Scholar
  47. 47.
    F. Sakr, M. Taher, A.Wahba, High performance iris recognition system on GPU, in Computer Engineering Systems (ICCES) (2011), 237–242Google Scholar
  48. 48.
    R.M. Stallman, Free Software, Free Society: Selected Essays of Richard M. Stallman, ed. by J. Gay (2002)Google Scholar
  49. 49.
    G. Sutra, S. Garcia-Salicetti, B. Dorizzi, The Viterbi algorithm at different resolutions for enhanced iris segmentation, in 2012 5th IAPR International Conference on Biometrics (ICB) (2012), pp. 310–316Google Scholar
  50. 50.
    A. Uhl, P. Wild, Combining face with face-part detectors under Gaussian assumption, in Proceedings of 9th International Conference on Image Analysis and Recognition, vol. 7325. LNCS, ed. by A. Campilho, M. Kamel (Springer, 2012), pp. 80–89Google Scholar
  51. 51.
    A. Uhl, P. Wild, Weighted adaptive hough and ellipsopolar transforms for real-time iris segmentation, in Proceedings of 5th International Conference on Biometrics (2012), pp. 1–8Google Scholar
  52. 52.
    C.J. van Rijsbergen, Information retrieval (Butterworth-Heinemann, 1979)Google Scholar
  53. 53.
    P. Vandewalle, J. Kovacevic, M. Vetterli, Reproducible research in signal processing. IEEE Signal Process. Mag. 26(3), 37–47 (2009)Google Scholar
  54. 54.
    G. Yang et al., SIFT based iris recognition with normalization and enhancement. Int. J. Mach. Learn. Cybern. 4(4), 401–407 (2013)Google Scholar
  55. 55.
    P.-F. Zhang, D.-S. Li, Q. Wang, A novel iris recognition method based on feature fusion, in Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 6 (2004), pp. 3661–3665Google Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Christian Rathgeb
    • 1
  • Andreas Uhl
    • 2
  • Peter Wild
    • 3
  • Heinz Hofbauer
    • 2
  1. 1.Biometrics and Internet Security Research GroupHochschule DarmstadtDarmstadtGermany
  2. 2.Multimedia Signal Processing and Security Lab, Department of Computer SciencesUniversity of SalzburgSalzburgAustria
  3. 3.Safety and Security DepartmentAIT Austrian Institute of Technology GmbHSeibersdorfAustria

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