Generation of artificial biometric data enhanced with contextual information for game strategy-based behavioral biometrics

Roman V. Yampolskiy, University of Louisville
Venu Govindaraju, University at Buffalo, The State University of New York


For the domain of strategy-based behavioral biometrics we propose the concept of profiles enhanced with spatial, temporal and contextual information. Inclusion of such information leads to a more stable baseline profile and as a result more secure systems. Such enhanced data is not always readily available and often is time consuming and expensive to acquire. One solution to this problem is the use of artificially generated data. In this paper a novel methodology for creation of feature-level synthetic biometric data is presented. Specifically generation of behavioral biometric data represented by game playing strategies is demonstrated. Data validation methods are described and encouraging results are obtained with possibility of expanding proposed methodologies to generation of artificial data in the domains other then behavioral biometrics.