Robust Gait Recognition by Integrating Inertial and RGBD Sensors

TL;DR

This paper introduces a multi-sensor gait recognition system combining inertial, RGB, and depth sensors, with new algorithms EigenGait and TrajGait, achieving higher accuracy and robustness than existing methods.

Contribution

It presents a novel multi-sensor fusion approach for gait recognition and introduces two new algorithms, EigenGait and TrajGait, for extracting gait features from inertial and RGBD data.

Findings

Higher recognition accuracy compared to state-of-the-art methods

Enhanced robustness under complex covariate conditions

Effective integration of inertial and RGBD sensors for gait analysis

Abstract

Gait has been considered as a promising and unique biometric for person identification. Traditionally, gait data are collected using either color sensors, such as a CCD camera, depth sensors, such as a Microsoft Kinect, or inertial sensors, such as an accelerometer. However, a single type of sensors may only capture part of the dynamic gait features and make the gait recognition sensitive to complex covariate conditions, leading to fragile gait-based person identification systems. In this paper, we propose to combine all three types of sensors for gait data collection and gait recognition, which can be used for important identification applications, such as identity recognition to access a restricted building or area. We propose two new algorithms, namely EigenGait and TrajGait, to extract gait features from the inertial data and the RGBD (color and depth) data, respectively. Specifically, EigenGait extracts general gait dynamics from the accelerometer readings in the eigenspace and TrajGait extracts more detailed sub-dynamics by analyzing 3D dense trajectories. Finally, both extracted features are fed into a supervised classifier for gait recognition and person identification. Experiments on 50 subjects, with comparisons to several other state-of-the-art gait-recognition approaches, show that the proposed approach can achieve higher recognition accuracy and robustness.