Privacy-Aware Gait Identification With Ultralow-Dimensional Data Using a Distance Sensor

Abstract

As one of the most natural user behaviors, walking has been widely focused on developing personal identification systems due to its unique biometric authentication features. Popular visual solutions are usually affected by various environmental conditions, and their redundant user information (e.g., body type and appearance) makes it more challenging for users to maintain privacy and security. This paper proposes a distance sensor–based gait identification system that uses only one-dimensional data with a simple system structure. Specifically, a time-of-flight sensor was placed in front of a walking person, and a time series of distances was acquired. We extracted gait features from the data by calculating the velocity and acceleration curves and identifying individuals using a random forest classifier. We evaluated our system on 10 users using leave-one-out cross-validation. The average identification accuracy was 91.05% for 10 users. This study shows that gait recognition is possible using only one-dimensional time-series data with a noncontact sensor. It can be used as a contactless identification, reducing the computational resources required for low-cost and low-power consumption edge computing.