We consider the problem of identifying people on the basis of their walk (gait) pattern. Classical approaches to tackle this problem are based on, e.g., video recordings or piezoelectric sensors embedded in the floor. In this work, we rely on acoustic and vibration measurements, obtained from a microphone and a geophone sensor, respectively. The contribution of this work is twofold. First, we propose a feature extraction method based on an (untrained) shallow scattering network, specially tailored for the gait signals. Second, we demonstrate that fusing the two modalities improves identification in the practically relevant open set scenario.
@article{arxiv.2001.08830,
title = {Scattering Features for Multimodal Gait Recognition},
author = {Srđan Kitić and Gilles Puy and Patrick Pérez and Philippe Gilberton},
journal= {arXiv preprint arXiv:2001.08830},
year = {2020}
}