Data fusion in biometrics

Any biometric system has drawbacks and cannot warranty 100% identification rates, nor 0% false acceptance and rejection ratios. One way to overcome the limitations is through a combination of different biometric systems. In addition, a multimodal biometric recognition is more difficult to fool than a single, biometric system, because it is more unlikely to defeat two or three biometric systems than one. This paper summarizes the different data fusion levels, and how it must be performed in order to improve the results of each combined system on its own

[1]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  M. Faundez-Zanuy,et al.  On the vulnerability of biometric security systems , 2004, IEEE Aerospace and Electronic Systems Magazine.

[4]  Frank K. Soong,et al.  On the use of instantaneous and transitional spectral information in speaker recognition , 1988, IEEE Trans. Acoust. Speech Signal Process..

[5]  Kuldip K. Paliwal,et al.  Information Fusion and Person Verification Using Speech & Face Information , 2002 .