Improving mouse dynamics biometric performance using variance reduction via extractors with separate features

Authors:
Youssef Nakkabi;Issa Traoré;Ahmed Awad E. Ahmed
Affiliations:
Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada;Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada;Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Year:
2010

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Abstract

The European standard for access control imposes stringent performance requirements on commercial biometric technologies that few existing recognition systems are able tomeet. In this correspondence paper, we present the first mouse dynamics biometric recognition system that fulfills this standard. The proposed system achieves notable performance improvement by developing separate models for separate feature groups involved. The improvements are achieved through the use of a fuzzy classification based on the Learning Algorithm for Multivariate Data Analysis and using a score-level fusion scheme to merge corresponding biometric scores. Evaluation of the proposed framework using mouse data from 48 users achieves a false acceptance rate of 0% and a false rejection rate of 0.36%.