  While developing continuous authentication systems (CAS), we generally assume
that samples from both genuine and impostor classes are readily available.
However, the assumption may not be true in certain circumstances. Therefore, we
explore the possibility of implementing CAS using only genuine samples.
Specifically, we investigate the usefulness of four one-class classifiers OCC
(elliptic envelope, isolation forest, local outliers factor, and one-class
support vector machines) and their fusion. The performance of these classifiers
was evaluated on four distinct behavioral biometric datasets, and compared with
eight multi-class classifiers (MCC). The results demonstrate that if we have
sufficient training data from the genuine user the OCC, and their fusion can
closely match the performance of the majority of MCC. Our findings encourage
the research community to use OCC in order to build CAS as they do not require
knowledge of impostor class during the enrollment process.
