Treadmill Assisted Gait Spoofing (TAGS)

Abstract

In this work, we examine the impact of Treadmill Assisted Gait Spoofing on Wearable Sensor-based Gait Authentication (WSGait). We consider more realistic implementation and deployment scenarios than the previous study, which focused only on the accelerometer sensor and a fixed set of features. Specifically, we consider the situations in which the implementation of WSGait could be using one or more sensors embedded into modern smartphones. In addition, it could be using different sets of features or different classification algorithms, or both. Despite the use of a variety of sensors, feature sets (ranked by mutual information), and six different classification algorithms, Treadmill Assisted Gait Spoofing was able to increase the average false accept rate from 4% to 26%. Such a considerable increase in the average false accept rate, especially under the stringent implementation and deployment scenarios considered in this study, calls for a further investigation into the design of evaluations of WSGait before its deployment for public use.