Abstract: Provided is a method for recognizing an individual by fusing a first match result generated by a first single-modal biometric recognition system of a first biometric modality and a second match result generated by at least one second single-modal biometric recognition system of a second biometric modality into a fused match result. Machine leaning based on training of retrieved data is performed to obtain optimized parameters meeting a target performance metric. Parameters of a fusion model are updated with the optimized parameters in a database, which is then applied to match results to recognize the individual. Other embodiments disclosed.

Abstract: Provided is a method for recognizing an individual by fusing a first match result generated by a first single-modal biometric recognition system of a first biometric modality and a second match result generated by at least one second single-modal biometric recognition system of a second biometric modality into a fused match result. Machine leaning based on training of retrieved data is performed to obtain optimized parameters meeting a target performance metric. Parameters of a fusion model are updated with the optimized parameters in a database, which is then applied to match results to recognize the individual. Other embodiments disclosed.

Abstract: A method for detecting a single or multi dimensional signal contaminated by additive Gaussian noise, and a detector using the method. The signal is an amplitude modulated sinusoid with known, but arbitrary, envelope and carrier frequency, but unknown uniform phase angle. The method comprises the step of calculating the integral for the likelihood ratio L ? ( X ) = 1 2 ? ? ? ? - ? ? ? L ? ? ( X ) ? ? ? ? ? analytically or numerically without limiting assumptions, and to make a comparison with a threshold value for deciding the question of detection.