CMC curve properties and biometric source weighting in multi-biometric score-level fusion

Multi-biometrics tries to build a unified biometric decision based on multiple biometric sources in an effort to gain more accuracy and robustness. Multi-biometric fusion aims at optimally combining the information produced by the multiple biometric sources, this usually requires assigning relative weights for the biometric sources to optimize their effect on the final decision. This work presents a new approach for biometric sources weighting within a score-level multi-biometric system. The presented solution tries to investigate the properties of the cumulative match characteristic (CMC) curve, which represents the biometric performance under the identification scenario, and extract biometric source weights based on those properties. The proposed solution is evaluated along with a set of state of the art and best practice weighting techniques. The evaluation was performed on the Biometric Scores Set BSSR1 database and a satisfying and stable performance was achieved.

I. Introduction

Multi-biometrics tries to use multiple biometric information sources to enhance performance and to overcome the limitations of the conventional uni-modal biometrics. Such limitations are noisy data, low distinctiveness, intra-user variation, non-universality of biometric characteristics, and vulnerability to spoof attacks.