Ensemble of Adaptive Algorithms for Keystroke Dynamics

Biometric systems have been applied to improve the security of several computational systems. These systems analyse physiological or behavioural features obtained from the users in order to perform authentication. Biometric features should ideally meet a number of requirements, including permanence. In biometrics, permanence means that the analysed biometric feature will not change over time. However, recent studies have shown that this is not the case for several biometric modalities. Adaptive biometric systems deal with this issue by adapting the user model over time. Some algorithms for adaptive biometrics have been investigated and compared in the literature. In machine learning, several studies show that the combination of individual techniques in ensembles may lead to more accurate and stable decision models. This paper investigates the usage of some ensemble approaches to combine the output of current adaptive algorithms for biometrics. The experiments are carried out on keystroke dynamics, a biometric modality known to be subject to change over time.