Machine Vision and Applications

, Volume 21, Issue 5, pp 695–711 | Cite as

On design and optimization of face verification systems that are smart-card based

  • Thirimachos Bourlai
  • Josef Kittler
  • Kieron Messer
Original Paper


The optimization of a smart card face verification system is a very complex process with many key factors to consider. It involves the investigation of the effect the system parameters have on the system performance measured in terms of accuracy and speed. As the parameters involved are not independent, the search space is of exponential complexity. In practice only partial optimization is feasible with many parameters forced to take default values. We argue that the key options to optimize are image resolution or/and image pre-processing. In addition the main design issue is the degree of compression that can be applied to the probe image before it is transmitted to the smart card. In this work we investigate different optimization strategies by considering both image compression and image resolution, and demonstrate that both the system performance and speed of access can be improved by the jointly optimized parameter setting and the level of probe compression. The experimental results obtained on the XM2VTS database suggest that the choice of one strategy over another is a matter of the time available for the system design, system performance, and response time.


Face verification Smart card System design Optimization 


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  1. 1.
    Biometric Solutions Incorporated: White paper: Why use a biometric and a card in the same device. (2002)
  2. 2.
    Bistarelli, S., Santini, F., Vaccarelli, A.: An asymmetric fingerprint matching algorithm for java card. In: 5th International Conference, AVBPA 2005, New York, pp. 279–288 (2005)Google Scholar
  3. 3.
    Bourlai, T., Kittler, J., Messer, K.: Database size effects on performance on a smart card face verification system. In: Proceedings of the International Conference on Automatic Face and Gesture Recognition, FG2006, 10–12 April 2006Google Scholar
  4. 4.
    Bourlai, T., Messer, K., Kittler, J.: Face verification system architecture using smart cards. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, 23–26 August 2004Google Scholar
  5. 5.
    Bourlai, T., Messer, K., Kittler, J.: Performance versus computational complexity trade-off in face verification. In: Proceedings of the International Conference on Biometric Authentication, ICBA 2004, 15–17 July 2004Google Scholar
  6. 6.
    Bourlai, T., Messer, K., Kittler, J.: Scenario based performance optimisation in face verification using smart cards. AVBPA 2005, 22–25 July 2005Google Scholar
  7. 7.
    Cai D., He X.F., Han J.W., Zhang H.-J.: Orthogonal laplacianfaces for face recognition. IEEE Trans. Image Process 15(11), 3608–3614 (2006)CrossRefGoogle Scholar
  8. 8.
    Carr, M.R.: Smart card technology with case studies. Security technology. In: Proceedings of the 36th Annual 2002 International Carnahan Conference on Security Technology, pp. 158–159, October 2002Google Scholar
  9. 9.
    Chen, H.-T., Chang, H.-W., Liu, T.-L.: Local discriminant embedding and its variants. CVPR 2005, vol. 2, pp. 846–853 (2005)Google Scholar
  10. 10.
    Cooper, D., Dang, H., Lee, P., MacGregor, W., Mehta, K.: Secure biometric match-on-card feasibility report. Technical Report. National Institute of Standards and Technology. Published as NIST Interagency Report 7452, November 2007Google Scholar
  11. 11.
    Czajka, A., Strzelczyk, P., Chochowski, M., Pacut, A.: Iris recognition with match-on-card. In: 15th European Signal Processing Conference, September 2007Google Scholar
  12. 12.
    Czyz, J., Bengio, S., Marchel, C., Vanderdorpe, L.: Scalability analysis of audio-visual person identity verification. In: 4th International Conference, AVBPA 2003, Guildford, UK, pp. 752–760, June 2003Google Scholar
  13. 13.
    Czyz, J., Vandendorpe, L.: Evaluation of lda-based face verification with respect to available computational resources. In: Int’l Workshop on Pattern Recognition in Information Systems (2002)Google Scholar
  14. 14.
    Deravi, F., Fairhurst, M.C., Ng, M.W.R., Gil, S., et al.: Report on biometric evaluation and system review of the finger card project. Research Report IST-2000-25168/D10.1/1.0, IST, European Union (2002)Google Scholar
  15. 15.
    European Commission JRC/IPTS: Biometrics at the frontiers: assessing the impact on society. Technical Report for the European Parliament Committee on Citizens’ Freedoms and Rights, Justice and Home Affairs (LIBE) (2005)Google Scholar
  16. 16.
    European Council: Council regulation (EC) no 2252/2004 of 13 December 2004 on standards for security features and biometrics in passports and travel documents issued by member states. EU (December 2004). (2004)
  17. 17.
    Flammang, M., Schmitz, P-E., Huijgens, R.: Trend report: biometrics in europe. UNISYS, European Biometrics Portal, pp. 12–13, 16–19 (2007)Google Scholar
  18. 18.
    Grother, P., Salamon, W., Watson, C., Indovina, M., Flanagan, P.: Minex ii: performance of fingerprint match-on-card algorithms. Phase II Report. NIST Interagency Report 7477 (2008)Google Scholar
  19. 19.
    HITACHI: Research and development. Hitachi Technology R&D Report (2007–2008)Google Scholar
  20. 20.
    Jain, A.K., Nandakumar, K., Nagar, A.: Biometric template security. EURASIP J. Adv. Signal Process., Hindawi Publishing Corporation, 17 (2008)Google Scholar
  21. 21.
    Jain A.K., Pankanti S.: A touch of money. IEEE Spectr. 3(7), 22–27 (2006)CrossRefGoogle Scholar
  22. 22.
    Kumar, P.Y., Ganesh, T.S.: Integration of smart card and gabor filter method based fingerprint matching for faster verification. INDICON, 2005 Annual IEEE, pp. 526–529, December 2005Google Scholar
  23. 23.
    Li, Y.P.: Linear discriminant analysis and its application to face identification. Ph.D. thesis, University of Surrey (2000)Google Scholar
  24. 24.
    Li, Y.P., Kittler, J., Matas, J.: Face verification using client specific fisher faces. In: Kent, J.T., Aykroyd, R.G. (eds.) Proceedings of the International Conference on the Statistics of Directions, Shapes and Images, pp. 63–66, September 2000Google Scholar
  25. 25.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: Xm2vtsdb: the extended m2vts database. AVBPA, pp. 72–77, March 1999Google Scholar
  26. 26.
    Moon H., Phillips P.J.: Analysis of PCA-based face recognition algorithms. In: Bowyer, K.W., Phillips, P.J. (eds) Empirical Evaluation Techniques in Computer Vision, pp. 57–71. IEEE Computer Science Press, New York (1998)Google Scholar
  27. 27.
    Moon H., Phillips P.J.: Computational and performance aspects of PCA-based face-recognition algorithms. Perception 30, 303–321 (2001)CrossRefGoogle Scholar
  28. 28.
    Naumann, I., Hogben, G.: Privacy features of European eid card specifications. European Network and Information Security Agency (ENISA). Originally appeared in the Elesevier Network Security Newsletter, ISSN 1353-485, pp. 9–13 (2008)Google Scholar
  29. 29.
    NIST: Federal information processing standard (FIPS) 201-1. NIST, Personal Identity Verification (PIV) of Federal Employees and Contractors (2006)Google Scholar
  30. 30.
    Noore A.: Highly robust biometric smart card. IEEE Trans. Consum. Electron 46(4), 1059–1063 (2000)CrossRefGoogle Scholar
  31. 31.
    Oman, H.: Conference report: Security Technology. In: The 36th International Carnahan Conference on Security Technology. In: IEEE AESS Systems Magazine, pp. 28–40, April 2003Google Scholar
  32. 32.
    Osborne, M., Ratha, N.K.: A JC-BIOAPI compliant smart card with biometrics for secure access control. In: 4th International Conference, AVBPA. Guildford, UK, pp. 903–910, June 2003Google Scholar
  33. 33.
    Sanchez-Reillo R.: Securing information and operations in a smart card through biometrics. IEEE Aerosp. Electron Syst. Mag. 16(4), 3–6 (2001)CrossRefGoogle Scholar
  34. 34.
    Sanchez-Reillo, R.: Including biometric authentication in a smart card operating system. In: Proceedings of the 3rd International Conference on Audio and Video-based Biometric Person Authentication (AVBPA). Halmstad, Suecia, pp. 342–347, 6–8 June 2001Google Scholar
  35. 35.
    Sanchez-Reillo, R., Mengibar-Pozo, L., Sanchez-Avila, C.: Microprocessor smart cards with fingerprint used authentication. IEEE AESM, pp. 22–24, March 2003Google Scholar
  36. 36.
    Sanchez-Reillo, R., Sanchez-Avila, C.: Iris recognition with low template size. In: Proceedings of the 3rd International Conference on Audio and Video-based Biometric Person Authentication (AVBPA). Halmstad, Suecia, pp. 324–329, 6–8 June 2001Google Scholar
  37. 37.
    Sanchez-Reillo R., Sanchez-Avila C.: Fingerprint verification using smart cards for access control systems. IEEE Aerosp. Electron Syst. Mag. 17(9), 12–15 (2002)CrossRefGoogle Scholar
  38. 38.
    Schouten, B.A.M., Tangelder, J.W.H.: Non-intrusive face verification by a virtual mirror interface using fractal codes. In: CD-ROM Proceedings Biometrics on the Internet—Third COST 275 Workshop (2005)Google Scholar
  39. 39.
    Seto, Y.: Development of personal authentication systems using fingerprint with smart cards and digital signature technologies. In: 7th International Conference on Control, Automation, Robotics and Vision (ICARCV 2002), vol. 2, pp. 996–1001, December 2002Google Scholar
  40. 40.
    Short, J., Kittler, J., Messer, K.: Photometric normalisation for face verification. AVBPA (2005)Google Scholar
  41. 41.
    Struif, B.: Use of biometrics for user verification in electronic signature smartcards. In: Proceedings of the International Conference on Research in Smart Cards (E-smart): Smart Card Programming and Security—Cannes, Frankreich, pp. 220–228, September 2001Google Scholar
  42. 42.
    Swets D., Weng J.: Using discriminant eigenfeatures for image retrieval. PAMI 18(8), 831–836 (1996)Google Scholar
  43. 43.
    Turk M., Pentland A.: Eigenfaces for recognition. Cogn. Neurosci. IEEE PAMI 3(1), 71–86 (1991)CrossRefGoogle Scholar
  44. 44.
    USA Department of Homeland Security: Twic reader hardware and card application specification. Department of Homeland Security Transportation Security Administration Transportation Threat Assessment and Credentialing Office 601 S. 12th Street Arlington, VA 22202, 28 March 2008Google Scholar
  45. 45.
    Walczowski, L.T., Deravi, F.: Training in the use of java smart cards for embedded applications. ICECS 2001, pp. 729–732, September 2001Google Scholar
  46. 46.
    Yambor W.S., Draper B.A., Beveridge J.R.: Analysing PCA-based face recognition algorithms: eigenvector selection and distance measures. In: Christensen, H., Phillips, J. (eds) Empirical Evaluation Methods in Computer Vision, World Scientific, Singapore (2002)Google Scholar
  47. 47.
    Zhao, W., Phillips, P.J., Chellappa, R., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv., 399–458 (2003)Google Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Thirimachos Bourlai
    • 1
  • Josef Kittler
    • 1
  • Kieron Messer
    • 1
  1. 1.Centre for Vision Speech and Signal ProcessingUniversity of SurreyGuildford, SurreyUK

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