Accelerometer based Gait Recognition using Adapted Gaussian Mixture Models

Abstract

Gait authentication using a cell phone based accelerometer sensor offers an unobtrusive, user-friendly, and a periodic way of authenticating individuals to their smartphones. In this paper, we present a GMM-UBM based gait recognition approach for a realistic scenario (when the phone is placed inside the trouser pocket and the user is walking) by using the magnitude data of a smartphone-based tri-axes accelerometer sensor. To evaluate our approach we use a gait data set of 35 participants collected at their respective normal walking pace in two different sessions with an average gap of 25 days between the sessions. We obtained EERs of 3.031%, 11.531%, and 14.393% for the same-day, mix-days, and cross-days, respectively.