Mathematics for multi-biometrics person recognition systems: Improving the performance of Biometric identification and authenticationsystems using advanced multimodal techniques

Biometrics is a scientific technology to recognize a person using their physical, behavior or chemical attributes. A system that uses asingle source of biometric information (e.g., single fingerprint) to recognize people is known as unimodal system. Whereas, the system that consolidates data from multiple biometric sources of information (e.g., face and fingerprint) is called multibiometrics system. that can alleviate the error rates and some inherent weaknesses of uni-biometrics.In this work, 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. Experimental results on three publicly available data sets for multi-modal,multi-unit and multi-algorithm systems show that presented WQAM fusion algorithm outperforms the previously previously proposed score fusion rules.In addition, a palm and wrist vein-based multi biometric system based on triangular norms is proposed.