Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Sclera recognition: on the quality measure and segmentation of degraded images captured under relaxed imaging conditions

© The Institution of Engineering and Technology
Received 10/07/2016, Accepted 17/11/2016, Revised 14/11/2016, Published 22/11/2016

The authors propose a new method for sclera quality measure and segmentation under relaxed imaging constraints. In particular, for sclera image, they propose a new quality measure approach based on a focus measure. In addition, they propose a new fusion method for sclera segmentation which uses pixel properties of both the sclera area and the skin around the eye. Furthermore, sclera template rotation alignment and distance scaling methods are proposed to minimise the error rates when noisy eye images are captured at-a-distance and on-the-move, together with overcoming head pose rotation. Then, a performance analysis on exploited eye images using the Excellent, the Good, the Bad, and the Ugly (EGBU) classification technique for image quality is used to evaluate system performance. Eye images captured under relaxed imaging constraints using four camera devices within the UBIRIS.v2 and MICHE mobile databases are utilised to evaluate the proposed sclera recognition system, with the UBIRIS.v1 database as a reference. Results in terms of sclera image quality measure and sclera segmentation are promising and describe the effect and challenges of using relaxed imaging conditions on sclera recognition system.

Inspec keywords: image segmentation; image classification; visual databases

Other keywords: image quality measure; head pose rotation; sclera segmentation; UBIRIS.v2 mobile databases; performance analysis; sclera image; focus measure; camera devices; EGBU classification technique; sclera recognition system; eye images; error rates minimisation; excellent the good the bad and the ugly classification technique; sclera template rotation alignment; fusion method; distance scaling methods; UBIRIS.v1 database; pixel properties; MICHE mobile databases; relaxed imaging conditions

Subjects: Image recognition; Spatial and pictorial databases; Computer vision and image processing techniques

References

    1. 1)
      • 1. Proenca, H.: ‘Iris recognition: on the segmentation of degraded images acquired in the visible wavelength’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (8), pp. 1502–1516.
    2. 2)
      • 24. Alkassar, S., Woo, W.L., Dlay, S.S., et al: ‘Enhanced segmentation and complex-sclera features for human recognition with unconstrained visible-wavelength imaging’. Nineth IAPR Int. Conf. on Biometrics (ICB), 2016, pp. 1–8.
    3. 3)
      • 3. Alkassar, S., Woo, W.L., Dlay, S.S., et al: ‘Robust sclera recognition system with novel sclera segmentation and validation techniques’, IEEE Trans. Syst. Man Cybern. Syst., 2016, (99), pp. 1–13.
    4. 4)
      • 10. Das, A., Pal, U., Ballester, M.A.F., et al: ‘A new efficient and adaptive sclera recognition system’. IEEE Symp. on Computational Intelligence in Biometrics and Identity Management (CIBIM), 2014, pp. 1–8.
    5. 5)
      • 15. Lin, Y., Du, E.Y., Zhou, Z., et al: ‘An Efficient Parallel Approach for Sclera Vein Recognition’. IEEE Trans. Inf. Forensics Security, 2014, 9, (2), pp. 147–157.
    6. 6)
      • 16. Hosseini, M.S., Araabi, B.N., Soltanian-Zadeh, H., et al: ‘Pigment Melanin: Pattern for Iris Recognition’. IEEE Trans. Instrum. Meas., 2010, 59, (4), pp. 792–804.
    7. 7)
      • 12. Das, A., Pal, U., Ferrerc, M.A., et al: ‘SSBC 2015: sclera segmentation benchmarking competition’. Seventh Int. Conf. on Biometrics Theory, Applications and Systems (BTAS), 2015, pp. 1–6.
    8. 8)
      • 19. Proenca, H., Filipe, S., Santos, R., 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., 2010, 32, (8), pp. 1529–1535.
    9. 9)
      • 2. Smereka, J.M., Boddeti, V.N., Vijaya Kumar, B.V.K.: ‘Probabilistic deformation models for challenging periocular image verification’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (9), pp. 1875–1890.
    10. 10)
      • 22. Pech-Pacheco, J.L., Cristóbal, G., Chamorro-Martinez, J., et al: ‘Diatom autofocusing in brightfield microscopy: a comparative study’. 15th Int. Conf. on Pattern Recognition, 2000, pp. 314–317.
    11. 11)
      • 25. Alkassar, S., Woo, W.L., Dlay, S.S., et al: ‘Efficient eye corner and gaze detection for sclera recognition under relaxed imaging constraints’. 24th European Signal Processing Conf. (EUSIPCO), 2016, pp. 1–5.
    12. 12)
      • 11. Gottemukkula, V., Saripalle, S., Tankasala, S.P., et al: ‘Method for using visible ocular vasculature for mobile biometrics’, IET Biometrics, 2016, 5, (1), pp. 3–12.
    13. 13)
      • 7. Crihalmeanu, S., Ross, A., Derakhshani, R.: ‘Enhancement and registration schemes for matching conjunctival vasculature’. Proc. of the Third IAPR/IEEE Int. Conf. on Biometrics (ICB), 2009, pp. 1240–1249.
    14. 14)
      • 14. Zhou, Z., Du, E.Y., Thomas, N.L.: ‘A comprehensive sclera image quality measure’. Eleventh Int. Conf. on Control Automation Robotics & Vision (ICARCV), 2010, pp. 638–643.
    15. 15)
      • 6. Derakhshani, R., Ross, A.: ‘A texture-based neural network classifier for biometric identification using ocular surface vasculature’. Int. Joint Conf. on Neural Networks (IJCNN), 2007, pp. 2982–2987.
    16. 16)
      • 5. Zhou, Z., Du, E.Y., Thomas, N.L., et al: ‘A new human identification method: sclera recognition’, IEEE Trans. Syst. Man Cybern. A, 2012, 42, (3), pp. 571–583.
    17. 17)
      • 13. Das, A., Pal, U., Ferrerc, M.A., et al: ‘SSRBC 2016: sclera segmentation and recognition benchmarking competition’. Ninth IAPR Int. Conf. on Biometrics (ICB), 2016, pp. 1–6.
    18. 18)
      • 17. Alkassar, S., Woo, W.L., Dlay, S.S., et al: ‘A novel method for sclera recognition with images captured on-the-move and at-a-distance’. Fourth Int. Conf. on Biometrics and Forensics (IWBF), 2016, pp. 1–6.
    19. 19)
      • 23. Daugman, J.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 21–30.
    20. 20)
      • 9. Oh, K., Toh, K.A.: ‘Extracting sclera features for cancelable identity verification’. Fifth IAPR Int. Conf. on Biometrics (ICB), 2012, pp. 245–250.
    21. 21)
      • 18. Proenca, H., Alexandre, L.A.: ‘UBIRIS: a noisy iris image database’. Int. Conf. on Image Anal. and Processing (ICIAP), 2005, pp. 970–977.
    22. 22)
      • 20. De Marsico, M., Nappi, M., Riccio, D., et al: ‘Mobile iris challenge evaluation (MICHE)-I, biometric iris dataset and protocols’, Pattern Recognit. Lett., 2015, 57, pp. 17–23.
    23. 23)
      • 8. Thomas, N.L., Du, E.Y., Zhou, Z.: ‘A new approach for sclera vein recognition’. SPIE Defense, Security, and Sensing, 2010.
    24. 24)
      • 21. Phillips, P.J., Beveridge, J.R., Draper, B.A., et al: ‘An introduction to the good, the bad, & the ugly face recognition challenge problem’. IEEE Int. Conf. on Automatic Face & Gesture Recognition and Workshops, 2011, pp. 346–353.
    25. 25)
      • 4. Derakhshani, R., Ross, A.: ‘A new biometric modality based on conjunctival vasculature’. Proc. of Artificial Neural Networks in Engineering (ANNIE), 2006, pp. 1–8.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2016.0114
Loading

Related content

content/journals/10.1049/iet-bmt.2016.0114
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address