Skip to main content
SpringerLink
Log in

Multispectral Ocular Biometrics

  • Chapter
  • First Online:
Face Recognition Across the Imaging Spectrum

Abstract

This chapter discusses the use of multispectral imaging to perform bimodal ocular recognition where the eye region of the face is used for recognizing individuals. In particular, it explores the possibility of utilizing the patterns evident in the sclera, along with the iris, in order to improve the robustness of iris recognition systems . Commercial iris recognition systems typically capture frontal images of the eye in the near-infrared spectrum . However, in non-frontal images of the eye, iris recognition performance degrades considerably. As the eyeball deviates away from the camera, the iris information in the image decreases, while the scleral information increases. In this work, we demonstrate that by utilizing the texture of the sclera along with the vascular patterns evident on it, the performance of an iris recognition system can potentially be improved. The iris patterns are better observed in near-infrared spectrum, while conjunctival vasculature patterns are better discerned in the visible spectrum. Therefore, multispectral images of the eye are used to capture the details of both the iris and the sclera. The contributions of this paper include (a) the assembly of a multispectral eye image collection to study the impact of intra-class variation on sclera recognition performance , (b) the design and development of an automatic sclera, iris, and pupil segmentation algorithm, and (c) the improvement of iris recognition performance by fusing the iris and scleral patterns in non-frontal images of the eye.

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

Access this chapter

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
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

Institutional subscriptions

Notes

  1. 1.

    http://wapedia.mobi/en/Eye_color.

  2. 2.

    http://www.timotheecour.com/software/ncut_multiscale/ncut_multiscale.html.

References

  1. Daugman, J.: How iris recognition works. IEEE Trans. Circuit Syst. Video Technol. 14(1), 21–30 (2004)

    Article  Google Scholar 

  2. Daugman, J.G.: The importance of being random: statistical principles of iris recognition. Pattern Recogn. 36, 279–291 (2003)

    Article  Google Scholar 

  3. Daugman, J.G.: Biometric personal identification system based on iris analysis, United States Patent 5,291,560

    Google Scholar 

  4. Ross, A.: Iris recognition: the path forward. IEEE Comput. 30–35 (2010)

    Google Scholar 

  5. Gorodnichy, D.O., Dubrofsky, E., Hoshino, R., Khreich, W., Granger, E., Sabourin, R.: Exploring the upper bound performance limit of iris biometrics using score calibration and fusion. IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 54–61 (2011)

    Google Scholar 

  6. Bowyer, K.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. Comput. Vis. Image Underst. 110(2), 281–307 (2008)

    Article  Google Scholar 

  7. Kalka, N.D., Zuo, J., Schmid, N.A., Cukic, B.: Estimating and fusing quality factors for iris biometric images. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(3), 509–524 (2010)

    Article  Google Scholar 

  8. Watson, P.G., Young, R.D.: Scleral structure, organization and disease. A review. Exp. Eye Res. 78(3), 609–623 (2004)

    Article  Google Scholar 

  9. Derakhshani, R., Ross, A., Crihalmeanu, S.: A new biometric modality based on conjunctival vasculature. In: Proceedings of Artificial Neural Networks in Engineering (ANNIE) (2006)

    Google Scholar 

  10. Crihalmeanu, S., Ross, A.: Multispectral scleral patterns for ocular biometric recognition. Pattern Recogn. Lett. (BIOCON) 33(14), 1860–1869 (2012)

    Article  Google Scholar 

  11. Derakhshani, R., Ross, A.: A Texture-based neural network classifier for biometric identification using ocular surface vasculature. In: International Joint Conference on Neural Networks (IJCNN), pp. 2982–2987 (2007)

    Google Scholar 

  12. Crihalmeanu, S., Ross, A., Derakhshani, R.: Enhancement and registration schemes for matching conjunctival vasculature. In: Proceedings of International Conference on Biometrics, Alghero, Italy, pp. 1240–1249 (2009)

    Google Scholar 

  13. Crihalmeanu, S., Ross, A.: On the use of multispectral conjunctival vasculature as a soft bio-metric. IEEE Workshop on Applications of Computer Vision (WACV), pp. 204–211 (2011)

    Google Scholar 

  14. Tankasala, S.P., Doynov, P., Derakhshani, R., Ross, A., Crihalmeanu, S.: Biometric recognition of conjunctival vasculature using GLCM features. In: International Conference on Image Information Processing (ICIIP), pp. 1–6 (2011)

    Google Scholar 

  15. Zhou, Z., Du, E.Y., Thomas, N.L., Delp, E.J.: A new human identification method: sclera recognition. IEEE Trans. Syst. Man Cybern. 42(3), 571–583 (2012)

    Article  Google Scholar 

  16. Tankasala, S.P., Doynov, P., Derakhshani, R.: Application of pyramidal directional filter for biometric identification using conjunctival vasculature pattern. In: IEEE International Conference on Technologies for Homeland Security (HST), pp. 639–644 (2013)

    Google Scholar 

  17. Das, A., Pal, U., Ferrer Ballester, M.A., Blumenstein, M.: A new efficient and adaptive sclera recognition system. In: IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 1–8 (2014)

    Google Scholar 

  18. Derakhshani, R., Saripalle, S., Doynov, P.: Computational methods for objective assessment of conjunctival vascularity. IEEE Eng. Med. Biol. Soc. (EMBC) 1490–1493 (2012)

    Google Scholar 

  19. Zhou, Z., Du, E.Y, Thomas, N.L.: A comprehensive sclera image quality measure. In: 11th International Conference on Control Automation Robotics Vision (ICARCV), pp. 638–643 (2010)

    Google Scholar 

  20. Du, E., Thomas, N.L., Delp, E.J.: Multi-angle sclera recognition system. IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 103–108 (2011)

    Google Scholar 

  21. Das, A., Pal, U., Ferrer Ballester, M.A., Blumenstein, M.: Multi-angle based lively sclera bio- metrics at a distance. In: IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM), pp. 22–29 (2014)

    Google Scholar 

  22. Gottemukkula, V., Saripalle, S.K., Tankasala, S.P., Derakhshani, R., Pasula, R., Ross, A.: Fusing iris and conjunctival vasculature: ocular biometrics in the visible spectrum. In: IEEE Conference on Technologies for Homeland Security (HST), pp. 150–155 (2012)

    Google Scholar 

  23. Tankasala, S.P., Doynov, P., Derakhshani, R.: Visible spectrum bi-modal ocular biometrics. In: 2nd International Conference on Communication Computing and Security, Vol. 6, pp. 564–573 (2012)

    Google Scholar 

  24. Zhou, Z., Du, E.Y., Thomas, N.L., Delp, E.J.: A comprehensive multimodal eye recognition. SIViP 7(4), 619–631 (2013)

    Article  Google Scholar 

  25. Wang, Y., Tan, T., Jain, A.K.: Combining face and iris biometrics for identity verification. In: Fourth International Conference on Audio-and Video-based Biometric Person Authentication (AVBPA), Guildford, UK, pp. 805–813 (2003)

    Google Scholar 

  26. Connaughton, R., Bowyer, K.W., Flynn, P.J.: Fusion of face and iris biometrics. Handbook of Iris Recognition, pp. 219–237 (2013)

    Google Scholar 

  27. Johnson, P.A., Hua, F., Schuckers, S.: Camparison of quality-based fusion of face and iris biometrics. In: IEEE International Joint Conference on Biometrics Compendium (IJCB), pp. 1–5 (2011)

    Google Scholar 

  28. Schuckers, S.A.C., Schmid, N.A., Abhyankar, A., Dorairaj, V., Boyce, C.K., Hornak, L.A.: On techniques for angle compensation in nonideal iris recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1176–1190 (2007)

    Article  Google Scholar 

  29. Gonzales, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall Inc., Upper saddle River, New Jersey 07458

    Google Scholar 

  30. Shah, S., Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. (TIFS) 4(4), 824–836 (2009)

    Article  Google Scholar 

  31. Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010)

    Article  Google Scholar 

  32. Boyce, C., Ross, A., Monaco, M., Hornak, L., Li, X.: Multispectral iris analysis: a preliminary study. In: Proceedings of Computer Vision and Pattern Recognition Workshop on Biometrics (CVPRW), pp. 51–60 (2006)

    Google Scholar 

  33. Selenick, I.W.: A new complex-directional wavelet transform and its application to image denoising. IEEE Int. Conf. Image Process. 3, 573–576 (2002)

    Article  Google Scholar 

  34. Viola, P., Jones, M.: Robust real-time face detection. Int. J. Comput. Vis. 57(2), 137–154 (2004)

    Article  Google Scholar 

  35. Guarneri, I., Guarnera, M., Messina, G., Tomaselli, V.: A signature analysis based method for elliptical shape. In: Digital Photography VI, SPIE Proceedings 7537 (75270L)

    Google Scholar 

  36. Li, X.: Modeling intra-class variation for non-ideal iris recognition. In: In Springer LNCS 3832: International Conference on Biometrics, pp. 419–427 (2006)

    Google Scholar 

  37. Cour, T., Benezit, F., Shi, J.: Spectral segmentation with multiscale graph decomposition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Vol. 2, pp. 1124–1131. IEEE Computer Society, Washington, DC, USA (2005)

    Google Scholar 

  38. Movellan, J.R.: Tutorial on Gabor filters. Technical Report, pp. 1–23 (2002)

    Google Scholar 

  39. Qiang, L., Shusuke, S., Kunio, D.: Selective enhancement filters for nodules, vessels, and airway walls in two or three dimensional CT scans. Med. Phys. 30(8), 2040–2051 (2003)

    Article  Google Scholar 

  40. Bay, H., Ess, A., Tuyleraars, T., Gool, L.V.: Speeded-up robust features. Comput. Vis. Image Underst. (CVIU) 110(3), 346–359 (2008)

    Article  Google Scholar 

  41. Park, U., Jillela, R., Ross, A., Jain, A.K.: Periocular biometrics in the visible spectrum. IEEE Trans. Inf. Forensics Secur. (TIFS) 6(1), 96–106 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Michigan State University, 428 South Shaw Lane, 3142 Engineering Building, East Lansing, MI, 48824, USA

    Simona G. Crihalmeanu & Arun A. Ross

Authors
  1. Simona G. Crihalmeanu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arun A. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Simona G. Crihalmeanu .

Editor information

Editors and Affiliations

  1. Multispectral Imagery Lab—MILab, LCSEE, West Virginia University, MORGANTOWN, West Virginia, USA

    Thirimachos Bourlai

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Crihalmeanu, S.G., Ross, A.A. (2016). Multispectral Ocular Biometrics. In: Bourlai, T. (eds) Face Recognition Across the Imaging Spectrum. Springer, Cham. https://doi.org/10.1007/978-3-319-28501-6_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28501-6_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28499-6

  • Online ISBN: 978-3-319-28501-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation