Can Chimeric Persons Be Used in Multimodal Biometric Authentication Experiments?

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3869)


Combining multiple information sources, typically from several data streams is a very promising approach, both in experiments and to some extent in various real-life applications. A system that uses more than one behavioral and physiological characteristics to verify whether a person is who he/she claims to be is called a multimodal biometric authentication system. Due to lack of large true multimodal biometric datasets, the biometric trait of a user from a database is often combined with another different biometric trait of yet another user, thus creating a so-called chimeric user. In the literature, this practice is justified based on the fact that the underlying biometric traits to be combined are assumed to be independent of each other given the user. To the best of our knowledge, there is no literature that approves or disapproves such practice. We study this topic from two aspects: 1) by clarifying the mentioned independence assumption and 2) by constructing a pool of chimeric users from a pool of true modality matched users (or simply “true users”) taken from a bimodal database, such that the performance variability due to chimeric user can be compared with that due to true users. The experimental results suggest that for a large proportion of the experiments, such practice is indeed questionable.


Gaussian Mixture Model Equal Error Rate Identity Match Biometric Authentication Multimodal User 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Person Identification in a Networked Society. Kluwer Publications, Dordrecht (1999)CrossRefGoogle Scholar
  2. 2.
    Ross, A., Jain, A., Qian, J.-Z.: Information Fusion in Biometrics. Pattern Recognition Letter 24(13), 2115–2125 (September 2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Fierrez-Aguilar, J., Ortega-Garcia, J., Gonzalez-Rodriguez, J., Bigun, J.: Kernel-Based Multimodal Biometric Verification Using Quality Signals. In: Defense and Security Symposium, Workshop on Biometric Technology for Human Identification, Proc. of SPIE, vol. 5404, pp. 544–554 (2004)Google Scholar
  4. 4.
    Dugelay, J.-L., Junqua, J.-C., Rose, K., Turk, M.: Workshop on Multimodal User Authentication (MMUA 2003), Santa Barbara, CA, December 11–12 (2003) (no publisher)Google Scholar
  5. 5.
    Kuncheva, L.I.: A Theoretical Study on Six Classifier Fusion Strategies. IEEE Trans. Pattern Analysis and Machine Intelligence 24(2), 281–286 (February 2002)CrossRefGoogle Scholar
  6. 6.
    Kittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On Combining Classifiers. IEEE Trans. Pattern Analysis and Machine Intelligence 20(3), 226–239 (1998)CrossRefGoogle Scholar
  7. 7.
    Poh, N., Bengio, S.: Improving Single Modal and Multimodal Biometric Authentication Using F-ratio Client Dependent Normalisation. Research Report 04-52, IDIAP, Martigny, Switzerland (2004)Google Scholar
  8. 8.
    Toh, K.-A., Jiang, X., Yau, W.-Y.: Exploiting Global and Local Decision for Multimodal Biometrics Verification. IEEE Trans. on Signal Processing 52(10), 3059–3072 (October 2004)CrossRefGoogle Scholar
  9. 9.
    Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  10. 10.
    Vapnik, V.N.: Statistical Learning Theory. Springer, Heidelberg (1998)zbMATHGoogle Scholar
  11. 11.
    Jain, A., Nandakumar, K., Ross, A.: Score Normalisation in Multimodal Biometric Systems. Pattern Recognition (to appear, 2005)Google Scholar
  12. 12.
    Ross, A., Govindarajan, R.: Feature Level Fusion Using Hand and Face Biometrics. In: Proc. SPIE Conf. on Biometric Technology for Human Identification II. LNCS, vol. 5779, Orlando, pp. 196–204 (2005)Google Scholar
  13. 13.
    Poh, N. , Bengio, S.: Database, Protocol and Tools for Evaluating Score-Level Fusion Algorithms in Biometric Authentication. Research Report 04-44, IDIAP, Martigny, Switzerland (2004) (Accepted for publication in AVBPA 2005) Google Scholar
  14. 14.
    Matas, J., Hamouz, M., Jonsson, K., Kittler, J., Li, Y., Kotropoulos, C., Tefas, A., Pitas, I., Tan, T., Yan, H., Smeraldi, F., Begun, J., Capdevielle, N., Gerstner, W., Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Comparison of Face Verification Results on the XM2VTS Database. In: Proc. 15th Int’l. Conf. Pattern Recognition, Barcelona, vol. 4, pp. 858–863 (2000)Google Scholar
  15. 15.
    Lüttin, J.: Evaluation Protocol for the XM2FDB Database (Lausanne Protocol). Communication 98-05, IDIAP, Martigny, Switzerland (1998)Google Scholar
  16. 16.
    Bolle, R.M., Ratha, N.K., Pankanti, S.: Error analysis of pattern recognition systems: the subsets bootstrap. Computer Visioin and Image Understanding 93(1), 1–33 (2004)CrossRefGoogle Scholar
  17. 17.
    Keller, M., Mariéthoz, J., Bengio, S.: Significance Tests for bizarre Measures in 2-Class Classification Tasks. IDIAP-RR 34, IDIAP (2004)Google Scholar
  18. 18.
    Bengio, S., Mariéthoz, J.: The Expected Performance Curve: a New Assessment Measure for Person Authentication. In: The Speaker and Language Recognition Workshop (Odyssey), Toledo, pp. 279–284 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.IDIAP Research InstituteMartignySwitzerland

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