Weighted quasi-arithmetic mean based score level fusion for multi-biometric systems

Biometrics is now being principally employed in many daily applications ranging from the border crossing to mobile user authentication. In the high-security scenarios, biometrics require stringent accuracy and performance criteria. Towards this aim, multi-biometric systems that fuse the evidences from multiple sources of biometric have exhibited to diminish the error rates and alleviate inherent frailties of the individual biometric systems. In this article, a novel scheme for score-level fusion based on weighted quasi-arithmetic mean (WQAM) has been proposed. Specifically, WQAMs are estimated via different trigonometric functions. The proposed fusion scheme encompasses properties of both weighted mean and quasi-arithmetic mean. Moreover, it does not require any leaning process. Experimental results on three publicly available data sets (i.e. NIST-BSSR1 Multimodal, NIST-BSSR1 Fingerprint and NIST-BSSR1 Face) for multi-modal, multi-unit and multi-algorithm systems show that presented WQAM fusion algorithm outperforms the previously proposed score fusion rules based on transformation (e.g. t-norms), classification (e.g. support vector machines) and density estimation (e.g. likelihood ratio) methods.