Journal of Signal Processing Systems

, Volume 91, Issue 2, pp 163–177 | Cite as

A New Framework for Match on Card and Match on Host Quality Based Multimodal Biometric Authentication

  • Mohammad Sabri
  • Mohammad-Shahram MoinEmail author
  • Farbod Razzazi


Smart cards are widely used to deploy secure and cost effective identity management systems. Integration of biometrics into the smart card leads to a strong two-factor authentication system through the match on card (MOC) process. Since MOC uses fixed authentication strategies during the life cycle of smart card, this leads to a low performance and high failure to acquire error in uncontrolled noisy environments. To solve this problem, this paper proposes a sequential quality based framework for biometric authentication. In the proposed framework a set of classifiers have been used to manage the workflow of the framework based on the quality of samples. Accordingly, subjects can be dynamically authenticated using MOC and MOH. A multimodal chimera database is used to evaluate this framework. Our findings indicate that the proposed approach provides higher accuracy than the unimodal MOC and MOH by 11.29% and 5.12%, respectively. Furthermore, the proposed framework can authenticate 83.85% of users without auxiliary trait at the expense of only 1.21% lower accuracy compared to parallel fusion, which require acquisition of all traits for entire users. Analysis of the results demonstrates that the proposed approach provides a compromise between accuracy, user convenience, security and system complexity.


Multimodal biometrics Authentication Match on card Score level fusion 


  1. 1.
    Nair, K.K., Helberg, A., Van der Merwe, J. (2016). An approach to improve the match-on-card fingerprint authentication system security. In 2016 Sixth international conference on digital information and communication technology and its applications (DICTAP), IEEE.Google Scholar
  2. 2.
    Theofanos, M., Garfinkel, S., Choong, Y.-Y. (2016). Secure and usable enterprise authentication: lessons from the field. IEEE Security & Privacy, 14.5, 14–21.CrossRefGoogle Scholar
  3. 3.
    Li, S.Z., & Jain, A. (2015). Encyclopedia of biometrics. Berlin: Springer Publishing Company, Incorporated.CrossRefGoogle Scholar
  4. 4.
    Ross, A.A., Nandakumar, K., Jain, A. (2006). Handbook of multibiometrics Vol. 6. Berlin: Springer Science & Business Media.Google Scholar
  5. 5.
    Woods, K., Philip Kegelmeyer, W., Bowyer, K. (1997). Combination of multiple classifiers using local accuracy estimates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19.4, 405–410.CrossRefGoogle Scholar
  6. 6.
    Marcialis, Gian Luca, Roli, Fabio, Didaci, Luca. (2009). Personal identity verification by serial fusion of fingerprint and face matchers. Pattern Recognition, 42.11, 2807–2817.CrossRefGoogle Scholar
  7. 7.
    Mueller, R., & Martini, U. (2006). Decision level fusion in standardized fingerprint match-on-card. In 9th international conference on control, automation, robotics and vision ICARCV’06, IEEE (p. 2006).Google Scholar
  8. 8.
    Vibert, B., Rosenberger, C., Security, A.N. (2013). Performance evaluation platform of biometric match on card. In 2013 (WCCIT) World congress on computer and information technology, IEEE.Google Scholar
  9. 9.
    Mlambo, C.S., & Shabalala, M.B. (2015). Distortion analysis on binary representation of minutiae based fingerprint matching for match-on-card. In 2015 IEEE symposium series on computational intelligence, IEEE.Google Scholar
  10. 10.
    Bistarelli, S., Santini, F., Vaccarelli, A. (2006). An asymmetric fingerprint matching algorithm for Java Card TM. Pattern Analysis and Applications, 9.4, 359–376.MathSciNetCrossRefGoogle Scholar
  11. 11.
    Fierrez-Aguilar, J. et al. (2005). Discriminative multimodal biometric authentication based on quality measures. Pattern Recognition, 38.5, 777–779.CrossRefGoogle Scholar
  12. 12.
    Raghavendra, R. et al. (2011). Designing efficient fusion schemes for multimodal biometric systems using face and palmprint. Pattern Recognition, 44.5, 1076–1088.CrossRefGoogle Scholar
  13. 13.
    Kittler, J. et al. (1998). On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20.3, 226–239.CrossRefGoogle Scholar
  14. 14.
    Vatsa, Mayank et al. (2010). On the dynamic selection of biometric fusion algorithms. IEEE Transactions on Information Forensics and Security, 5.3, 470–479.CrossRefGoogle Scholar
  15. 15.
    Bhatt, Himanshu S. et al. (2011). A framework for quality-based biometric classifier selection. In 2011 international joint conference on biometrics (IJCB), IEEE.Google Scholar
  16. 16.
    Baig, Asim et al. (2014). Cascaded multimodal biometric recognition framework. IET Biometrics, 3.1, 16–28.CrossRefGoogle Scholar
  17. 17.
    Poh, N. et al. (2009). Benchmarking quality-dependent and cost-sensitive score-level multimodal biometric fusion algorithms. IEEE Transactions on Information Forensics and Security, 4.4, 849–866.CrossRefGoogle Scholar
  18. 18.
    Marcialis, Gian Luca, Mastinu, Paolo, Roli, F. (2010). Serial fusion of multi-modal biometric systems. In 2010 IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS), IEEE (p. 2010).Google Scholar
  19. 19.
    Vatsa, M., Singh, R., Noore, A. (2009). Context switching algorithm for selective multibiometric fusion. In International Conference on Pattern Recognition and Machine Intelligence (pp. 452–457): Springer.Google Scholar
  20. 20.
    Bharadwaj, S. et al. (2015). QFUse: Online learning framework for adaptive biometric system. Pattern Recognition, 48.11, 3428–3439.CrossRefGoogle Scholar
  21. 21.
    Lumini, A., & Nanni, L. (2017). Overview of the combination of biometric matchers. Information Fusion, 33, 71–85.CrossRefGoogle Scholar
  22. 22.
    Bzdok, D., Krzywinski, M., Altman, N. (2018). Machine learning: Supervised methods, SVM and kNN. Nature Methods, 1–6.Google Scholar
  23. 23.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J. (2003). A practical guide to support vector classification 1–16.Google Scholar
  24. 24.
    Ulery, Brad et al. (2006). Studies of biometric fusion. NIST Interagency Report 7346.Google Scholar
  25. 25.
    Hampel, Frank R. et al. (2011). Robust statistics: the approach based on influence functions Vol. 114. Hoboken: Wiley.Google Scholar
  26. 26.
    Jain, A., Nandakumar, K., Ross, A. (2005). Score normalization in multimodal biometric systems. Pattern Recognition, 38.12, 2270–2285.CrossRefGoogle Scholar
  27. 27.
    Nandakumar, Karthik et al. (2008). Likelihood ratio-based biometric score fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30.2, 342–347.CrossRefGoogle Scholar
  28. 28.
    McLachlan, G., & Peel, D. (2004). Finite mixture models. Hoboken: Wiley.zbMATHGoogle Scholar
  29. 29.
    Chen, Y., Dass, S.C., Jain, A.K. (2005). Fingerprint quality indices for predicting authentication performance. In International conference on audio-and video-based biometric person authentication. Berlin: Springer.Google Scholar
  30. 30.
    Maio, D. et al. (2004). FVC2004: Third fingerprint verification competition. In Biometric Authentication (pp. 1–7): Springer.Google Scholar
  31. 31.
    Maio, Dario et al. (2002). FVC2000: Fingerprint Verification competition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24.3, 402–412.CrossRefGoogle Scholar
  32. 32.
    Maio, D. et al. (2002). FVC2002: Second Fingerprint verification competition. In 16th international conference on pattern recognition, (2002). Proceedings, vol. 3. IEEE.Google Scholar
  33. 33.
    Cappelli, R., Ferrara, M., Franco, A., Maltoni, D. (2007). Fingerprint verification competition 2006. Biometric Technology Today, 15(7–8), 7–9.CrossRefGoogle Scholar
  34. 34.
    Thomaz, Carlos Eduardo, & Giraldi, Gilson Antonio. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, 28.6, 902–913.CrossRefGoogle Scholar
  35. 35.
    Guest, R. (2011). Information technology–Biometric data interchange formats–19794-Part 2: Finger minutiae data.Google Scholar
  36. 36.
    Watson, Craig I. et al. (2015). Fingerprint vendor technology evaluation NIST Interagency/Internal Report (NISTIR)-8034.Google Scholar
  37. 37.
    Olsen, M.A., Smida, V., Busch, C. (2016). Finger image quality assessment features definitions and evaluation. IET Biometrics, 5.2, 47–64.CrossRefGoogle Scholar
  38. 38.
    Chinese Academy of Sciences, Institute of Automation (CASIA),

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer Engineering, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.ICT Research Institute (ITRC)TehranIran

Personalised recommendations