Machine Vision and Applications

, Volume 27, Issue 1, pp 65–76 | Cite as

On the use of fingernail images as transient biometric identifiers

Biometric recognition using fingernail images
  • Igor Barros Barbosa
  • Theoharis Theoharis
  • Ali E. Abdallah
Original Paper

Abstract

The significant advantages that biometric recognition technologies offer are in danger of being left aside in everyday life due to concerns over the misuse of such data. The biometric data employed so far focuses on the permanence of the characteristics involved. A concept known as ‘the right to be forgotten’ is gaining momentum in international law and this should further hamper the adoption of permanent biometric recognition technologies. However, a multitude of common applications are short-term and, therefore, non-permanent biometric characteristics would suffice for them. In this paper we discuss ‘transient biometrics,’ i.e. recognition via biometric characteristics that will change in the short term and show that images of the fingernail plate can be used as a transient biometric with a useful life-span of less than 6 months. A direct approach is proposed that requires no training and a relevant evaluation dataset is made publicly available.

Keywords

Biometric recognition systems Fingernail Non-critical application Transient biometric characteristics  Biometric access control Feature extraction Image segmentation 

References

  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell 28(12), 2037–2041 (2006). doi:10.1109/TPAMI.2006.244 CrossRefGoogle Scholar
  2. 2.
    Barbosa, I.B., Theoharis, T., Schellewald, C., Athwal, C.: Transient biometrics using finger nails. In: Biometrics: theory, applications and systems (BTAS), 2013 IEEE Sixth International Conference on, pp. 1–6 (2013). doi:10.1109/BTAS.2013.6712730
  3. 3.
    Bazzani, L., Cristani, M., Murino, V.: Symmetry-driven accumulation of local features for human characterization and re-identification. Comput. Vis. Image Underst. 117(2), 130–144 (2013)CrossRefGoogle Scholar
  4. 4.
    Chornenky, T.: United states patent us20030098774 (2001). https://www.google.no/patents/US20030098774
  5. 5.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Fujishima, N., Hoshino, K.: Fingernail detection system using differences of the distribution of the nail-color pixels. JACIII 17(5), 739–745 (2013)Google Scholar
  7. 7.
    Grieve, T., Lincoln, L., Sun, Y., Hollerbach, J., Mascaro, S.: 3d force prediction using fingernail imaging with automated calibration. In: Haptics Symposium, 2010 IEEE, pp. 113–120 (2010). doi:10.1109/HAPTIC.2010.5444669
  8. 8.
    Hamdoun, O., Moutarde, F., Stanciulescu, B., Steux, B.: Person re-identification in multi-camera system by signature based on interest point descriptors collected on short video sequences. In: 2nd ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC-08). Stanford, Palo Alto, États-Unis (2008)Google Scholar
  9. 9.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey vision conference, vol. 15, p. 50. Manchester (1988)Google Scholar
  10. 10.
    Kale, K., Rode, Y., Kazi, M., Dabhade, S., Chavan, S.: Multimodal biometric system using fingernail and finger knuckle. In: Computational and Business Intelligence (ISCBI), 2013 International Symposium on, pp. 279–283 (2013). doi:10.1109/ISCBI.2013.63
  11. 11.
    Krstic, R.: Human microscopic anatomy: an atlas for students of medicine and biology. Springer (1991)Google Scholar
  12. 12.
    Kumar, A., Garg, S., Hanmandlu, M.: Biometric authentication using finger nail plates. 1. Expert Syst. Appl. 41(2), 373–386 (2014). doi:10.1016/j.eswa.2013.07.057 CrossRefGoogle Scholar
  13. 13.
    Kumar, A., Ravikanth, C.: Personal authentication using finger knuckle surface. Inf. Forensics Secur. IEEE Trans. 4(1), 98–110 (2009). doi:10.1109/TIFS.2008.2011089 CrossRefGoogle Scholar
  14. 14.
    Leutenegger, S., Chli, M., Siegwart, R.Y.: Brisk: binary robust invariant scalable keypoints. Comput. Vis. IEEE Int. Conf. 0, 2548–2555 (2011). doi:10.1109/ICCV.2011.6126542
  15. 15.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Image Processing, 2002. Proceedings. 2002 International Conference on, vol. 1, pp. I–900–I–903 (2002). doi:10.1109/ICIP.2002.1038171
  16. 16.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004). doi:10.1023/B:VISI.0000029664.99615.94 CrossRefGoogle Scholar
  17. 17.
    Mantelero, A.: The eu proposal for a general data protection regulation and the roots of the ’right to be forgotten’. Comput. Law Secur. Rev. 29(3), 229–235 (2013)CrossRefGoogle Scholar
  18. 18.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International conference on computer vision theory and application VISSAPP’09, INSTICC Press, pp. 331–340 (2009)Google Scholar
  19. 19.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996). doi:10.1016/0031-3203(95)00067-4
  20. 20.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefGoogle Scholar
  21. 21.
    Perakis, P., Theoharis, T., Kakadiaris, I.A.: Feature fusion for facial landmark detection. Pattern Recogn. 47(9), 2783–2793 (2014). doi:10.1016/j.patcog.2014.03.007
  22. 22.
    Ratha, N.K., Connell, J.H., Bolle, R.M.: Enhancing security and privacy in biometrics-based authentication systems. IBM Syst. J. 40(3), 614–634 (2001). doi:10.1147/sj.403.0614 CrossRefGoogle Scholar
  23. 23.
    Rathgeb, C., Uhl, A.: A survey on biometric cryptosystems and cancelable biometrics. EURASIP J. Inf. Secur. 2011(1), 3 (2011). doi:10.1186/1687-417X-2011-3 CrossRefGoogle Scholar
  24. 24.
    Shi, J., Tomasi, C.: Good features to track. In: Computer vision and pattern recognition, 1994. Proceedings CVPR ’94., 1994 IEEE Computer Society Conference on, pp. 593–600 (1994). doi:10.1109/CVPR.1994.323794
  25. 25.
    Sun, Y., Hollerbach, J.M., Mascaro, S.A.: Measuring fingertip forces by imaging the fingernail. IEEE Computer Society, Los Alamitos, p. 20 (2006). doi:10.1109/VR.2006.97
  26. 26.
    Topping, A., Kuperschmidt, V., Gormley, A.: United States Patent US005751835A (1998)Google Scholar
  27. 27.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Computer vision and pattern recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1, pp. I–511–I–518 (2001). doi:10.1109/CVPR.2001.990517
  28. 28.
    Yaemsiri, S., Hou, N., Slining, M., He, K.: Growth rate of human fingernails and toenails in healthy american young adults. J. Eur. Acad. Dermatol. Venereol. 24(4), 420–423 (2010). doi:10.1111/j.1468-3083.2009.03426.x CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Department of Computer and Information ScienceNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Birmingham City UniversityBirminghamUK

Personalised recommendations