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.
Highlights
Biometric systems refer to the identification of human beings by their physical or behavioral traits [1].These traits have the advantage that they are unique and permanent, unlike conventional techniques, such as passwords and badges, which can be used fraudulently by others
In a previous work [23], we introduced a general framework of a multibiometric identification system based on fusion at the matching score level using fuzzy set theory
We evaluated the performance of our method on the publicly-available benchmark database for score level fusion BSSR1 distributed by NIST [33]
Summary
Biometric systems refer to the identification of human beings by their physical or behavioral traits [1]. These traits have the advantage that they are unique and permanent, unlike conventional techniques, such as passwords and badges, which can be used fraudulently by others. Information 2015, 6 context, biometric systems can operate in two modes, namely verification and identification [2]. The system validates a query biometric by comparing only the captured biometric data with the biometric template of a specific identity stored in the database. The user’s biometric input is compared to the templates of all persons enrolled in the database. The identification task is significantly more challenging than verification [3]
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