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

access icon free Radial correlations in iris patterns, and mutual information within IrisCodes

© The Institution of Engineering and Technology
Received 31/08/2018, Accepted 15/10/2018, Revised 05/10/2018, Published 02/11/2018

The discriminating powers of biometric patterns derive from their entropy, just as the hardness of cryptographic keys derive from their entropy. The larger the number of independent bits, or the more independent they are, the less chance of collision. The authors measured the mutual information entailed by radial correlations within each of 632,500 different iris patterns from persons of 152 nationalities. For each iris, they measured how well the sequence of bits in any ring of the IrisCode predicts the sequence of bits in the other rings. Information density is quite non-uniformly distributed across iris patterns radially. Their measurements of mutual information address how much radial resolution is productive to use when encoding an iris, and they show that a non-uniform allocation of encoding resolution radially leads to significant performance improvements by reducing redundancy.

Inspec keywords: image recognition; entropy; image coding; iris recognition; biometrics (access control); image matching

Other keywords: radial resolution; discriminating powers; radial correlations; IrisCode; independent bits; mutual information; iris patterns; information density; cryptographic keys; biometric patterns

Subjects: Computer vision and image processing techniques; Image recognition; Data security; Image and video coding

References

    1. 1)
      • 5. Depeursinge, A., Al-Kadi, O., Mitchell, J.: ‘Biomedical texture analysis: fundamentals, applications, tools and challenges’ (Elsevier, London, UK, 2017).
    2. 2)
      • 3. Haralick, R.M., Shanmugam, K., Dinstein, I.: ‘Textural features for image classification’, IEEE Trans. Syst. Man Cybern., 1973, 3, (6), pp. 610–621.
    3. 3)
      • 9. Grother, P., Quinn, G.W., Matey, J.R., et al: ‘IREX-III: performance of iris identification algorithms’. NIST Interagency Report 7836, NIST, Gaithersburg, MD, 6 April 2012.
    4. 4)
      • 2. Wang, L., He, D.C.: ‘Texture classification using texture spectrum’, Pattern Recognit. Lett., 1990, 13, pp. 905–910.
    5. 5)
      • 8. Daugman, J.G.: ‘Information theory and the IrisCode’, IEEE Trans. Inf. Forensics Secur., 2015, 11, (2), pp. 400–409.
    6. 6)
      • 4. Zucker, S.W., Terzopoulos, D.: ‘Finding structure in co-occurrence matrices for texture analysis’, Comput. Graph. Image Process., 1980, 2, pp. 286–308.
    7. 7)
      • 10. Daugman, J.G.: ‘How iris recognition works’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (1), pp. 21–30.
    8. 8)
      • 6. Shannon, C.E.: ‘A mathematical theory of communication’, Bell Syst. Tech. J., 1948, 27, pp. 379–423.
    9. 9)
      • 7. Cover, T., Thomas, J.: ‘Elements of information theory’ (Wiley-Interscience, Hoboken, New Jersey, USA, 2006, 2nd edn.).
    10. 10)
      • 1. Hu, Y., Sirlantzis, K., Howells, G.: ‘A study on iris textural correlation using steering kernels’. IEEE 8th Int. Conf. on Biometrics Theory, Applications and Systems (BTAS), Buffalo, New York, USA, 2016, pp. 1–8, doi: 10.1109/BTAS.2016.7791160.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5199
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5199
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address