access icon free Authentic mobile-biometric signature verification system

This is an undeniable fact that in the coming years a considerable percentage of organisations are drifting toward mobile devices for authentication. Banking sector as an additional offshoot has shifted to mobile devices with their applications for e-banking and mobile-banking, giving rise to an emergent requirement of a foolproof and authentic mobile-biometric system. This study presents an authentic mobile-biometric signature verification system and a comparative analysis of the performance of the proposed system for the two datasets; one using the standard device that is used for capturing biometric signatures and the other one is a mobile database taken from a smart phone for biometric signature authentication. The results presented demonstrate that the proposed system outperforms existing mobile-biometric signature verification systems based on dynamic time warping and hidden Markov model. Moreover, this study presents a comprehensive survey of mobile-biometric systems, different devices and hardware needed to support mobile biometrics along with open issues and challenges faced by the mobile-biometric systems. The experiments presented establish that the performance of mobile devices is low as compared with normal biometric signature capturing devices and the major reason the authors found is the absence of pen-tilt angle information in the mobile device datasets.

Inspec keywords: mobile computing; hidden Markov models; digital signatures; smart phones; handwriting recognition; dynamic programming

Other keywords: dynamic time warping; smart phone; hidden Markov model; signature authentication; mobile-biometric signature verification system; mobile device

Subjects: Markov processes; Mobile, ubiquitous and pervasive computing; Data security; Optimisation techniques

References

    1. 1)
      • 29. Das, R.: ‘An introduction to biometrics’, Mil. Technol., 2005, 29, (7), pp. 2027.
    2. 2)
      • 19. Kabai, S.: ‘Gyroscope’. Available at http://www.demonstrations.wolfram.com/Gyroscope/, Wolfram Demonstrations Project Published, 28 September 2007.
    3. 3)
      • 3. Zareen, F.J., Jabin, S.: ‘A comparative study of the recent trends in biometric signature verification’. 2013 Sixth Int. Conf. on Contemporary Computing (IC3), August 2013, pp. 354358.
    4. 4)
      • 23. Mitchell, T.M.: ‘Machine learning. 1997’ (McGraw-Hill, Burr Ridge, IL, 1997), vol. 45.
    5. 5)
      • 6. Marcel, S., McCool, C., Atanasoaei, C., et al: ‘MOBIO: mobile biometric face and speaker authentication’. Idiap, No. EPFL-REPORT-70604, 2010.
    6. 6)
      • 15. Alonso-Fernandez, F., Fierrez-Aguilar, J., Ortega-Garcia, J.: ‘Sensor interoperability and fusion in signature verification: A case study using tablet pc’, in ‘Advances in biometric person authentication’ (Springer, Berlin, Heidelberg, 2005), pp. 180187.
    7. 7)
      • 9. Sieger, H., Kirschnick, N., Möller, S.: ‘User preferences for biometric authentication methods and graded security on mobile phones’. Proc. Symp. on Usability, Privacy, and Security (SOUPS), 2010.
    8. 8)
      • 1. Bigun, J., Fiérrez-Aguilar, J., Ortega-Garcia, J., et al: ‘Multimodal biometric authentication using quality signals in mobile communications’. Proc. Int. Conf. on in Image Analysis and Processing IEEE Computer Society, September 2003, pp. 212.
    9. 9)
      • 26. Kisi, Ö., Uncuoglu, E.: ‘Comparison of three back-propagation training algorithms for two case studies’, Indian J. Eng. Mater. Sci., 2005, 12, (5), pp. 434442.
    10. 10)
      • 25. Jabin, S.: ‘Stock market prediction using feed-forward artificial neural network’, Int. J. Comput. Appl. (IJCA), 2014, 99, (9), pp. 48.
    11. 11)
    12. 12)
      • 20. Goodrich, R.: ‘Accelerometer vs. gyroscope: what's the difference?’, LiveScience Contributor.
    13. 13)
      • 5. Tao, Q., Veldhuis, R.: ‘Biometric authentication for a mobile personal device’. Proc. Third Annual Int. Conf. on Mobile and Ubiquitous Systems-Workshops IEEE, July 2006, pp. 13.
    14. 14)
      • 10. The global biometrics and mobility report: the convergence of commerce and privacy’. Available at http://www.acuity-mi.com/GBMR_Report.php#sthash.d75xo10B.dpuf, accessed March 2015.
    15. 15)
    16. 16)
      • 12. Ailisto, H.J., Lindholm, M., Mantyjarvi, J., et al: ‘Identifying people from gait pattern with accelerometers’. Proc. SPIE, March 2005, pp. 714.
    17. 17)
    18. 18)
      • 8. Meng, Y., Wong, D.S., Schlegel, R.: ‘Touch gestures based biometric authentication scheme for touchscreen mobile phones’. Information Security and Cryptology, Berlin, Heidelberg, January 2013, pp. 331350.
    19. 19)
    20. 20)
      • 14. Krish, R.P., Fierrez, J., Galbally, J., et al: ‘Dynamic signature verification on smart phones’. Proc. Highlights on Practical Applications of Agents and Multi-Agent Systems, Berlin, Heidelberg, 2013, pp. 213222.
    21. 21)
    22. 22)
      • 7. Trewin, S., Swart, C., Koved, L., et al: ‘Biometric authentication on a mobile device: a study of user effort, error and task disruption’. Proc. 28th Annual Computer Security Applications Conf., December. ACM, 2012, December 2012, pp. 79168.
    23. 23)
    24. 24)
    25. 25)
    26. 26)
      • 28. Refaeilzadeh, P., Tang, L., Liu, H.: ‘Cross-validation’, in Liu, L., Özsu, M.T. (Eds.): ‘Encyclopedia of database systems’ (Springer, US, 2009), pp. 532538.
    27. 27)
    28. 28)
    29. 29)
      • 24. SVC 2004 database. Available at https://www.aut.bme.hu/Pages/Research/Signature/Resources.
    30. 30)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2015.0017
Loading

Related content

content/journals/10.1049/iet-bmt.2015.0017
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading