Performance Evaluation of Gaussian Mixture Models for Inertial Sensor-based Gait Biometrics

Abstract

Portable sensor-based gait biometrics has been actively researched in recent years. Generative models are rarely examined as part of gait recognition systems and there are several open questions with regards to machine learning approaches in this context. This paper shows that cross-day evaluations and the selection method of impostors influence system performance the most, and non-adapted Gaussian Mixture Models perform slightly better than adapted ones.