User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted
degrees of freedom, non-universality of the biometric trait and unacceptable error rates. Attempting to improve
the performance of individual matchers in such situations may not prove to be effective because of these inherent
problems. Multibiometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the
same identity. These systems help achieve an increase in performance that may not be possible using a single biometric
indicator. Further, multibiometric systems provide anti-spoofing measures by making it difficult for an intruder to
spoof multiple biometric traits simultaneously. However, an effective fusion scheme is necessary to combine the information
presented by multiple domain experts. This work addresses the problem of information fusion in biometric
Depending upon the sources of information being consolidated, a multibiometric system can be characterized as a multi-sample,
multi-sensor, multi-algorithm, multi-instance (or multi-unit), or multimodal system. Furthermore, there are various levels of
fusion possible in such a system, viz., raw data level, feature level, score level, rank level, and decision level.
A. Ross, "
An Introduction to Multibiometrics," Proc. of the 15th European Signal Processing Conference (EUSIPCO), (Poznan, Poland), September 2007.
A. Ross, K. Nandakumar and A. K. Jain, "Handbook of
Multibiometrics", Springer Publishers, 1st edition, 2006.
A. K. Jain and A. Ross, "Multibiometric Systems",
Communications of the ACM, Special Issue on Multimodal
Interfaces, Vol. 47, No. 1, pp. 34-40, January 2004.
A. Ross and A. K. Jain, "
Information Fusion in Biometrics",
Pattern Recognition Letters, Vol. 24, Issue 13, pp. 2115-2125,
A. Ross, A. K. Jain, and J. Qian, "
Information Fusion in
Biometrics", Proc. of 3rd Int'l Conference on Audio- and
Video-Based Person Authentication (AVBPA), pp. 354-359, Sweden, June 6-8, 2001.