Abstract: This paper presents a new biometric score fusion approach in an identification system using the upper integral with respect to Sugeno's fuzzy measure. First, the proposed method considers each individual matcher as a fuzzy set in order to handle uncertainty and imperfection in matching scores. Then, the corresponding fuzzy entropy estimates the reliability of the information provided by each biometric matcher. Next, the fuzzy densities are generated based on rank information and training accuracy. Finally, the results are aggregated using the upper fuzzy integral. Experimental results compared with other fusion methods demonstrate the good performance of the proposed approach.
In another work, Fakhar et al. REF proposed the use of the training accuracy and the fuzzy entropy (the reliability of information provided by each information source) to generate fuzzy densities followed by aggregation using the CFI.
Applying the Upper Integral to the Biometric Score Fusion Problem in the Identification Model
Information
Information
Computer Science