  Semi-supervised template update systems allow to automatically take into
account the intra-class variability of the biometric data over time. Such
systems can be inefficient by including too many impostor's samples or skipping
too many genuine's samples. In the first case, the biometric reference drifts
from the real biometric data and attracts more often impostors. In the second
case, the biometric reference does not evolve quickly enough and also
progressively drifts from the real biometric data. We propose a hybrid system
using several biometric sub-references in order to increase per- formance of
self-update systems by reducing the previously cited errors. The proposition is
validated for a keystroke- dynamics authentication system (this modality
suffers of high variability over time) on two consequent datasets from the
state of the art.
