Multi-Biometrics: Keystroke and Mouse Motion Fusion at the Classification Output Level

Abstract

Over the past several years and with the growth of technology, the field of biometrics has become very important in user identity and authentication. Technology keeps evolving and every day systems become more complex. There is a growing need for these systems to be more secure and reliable, and multi-biometrics is often both. Identifying biometric traits that provide reliable data, as well as studying and analyzing different fusion algorithms for combining these data, can result in better authentication techniques for lower EER% and potentially even better biometric systems. This paper will investigate and analyze fusion methods including the Minimum, Maximum, Mean, and Median strategies, using various Python scientific libraries and techniques, to design a system that combines classification output data of two or more biometric systems. These techniques can potentially result in an improved performance of multibiometric systems over that of an individual biometric system. Verification was performed using the Pace University Biometric System (PBS) and the Pace University Keystroke Biometric System (PKBS) to collect the data being investigated. 1.

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Cite this paper

@inproceedings{Breuer2015MultiBiometricsKA, title={Multi-Biometrics: Keystroke and Mouse Motion Fusion at the Classification Output Level}, author={Todd Breuer and Paola Garcia Cardenas and Anu George and Hung Le and Leigh Anne Clevenger and John V. Monaco}, year={2015} }