Hybrid template update system for unimodal biometric systems

Abstract

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 performance 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.