Relative distance features for gait recognition with Kinect

TL;DR

This paper introduces a novel relative distance-based gait feature for Kinect-based human recognition, demonstrating high accuracy and potential for broader applications beyond Kinect.

Contribution

The study proposes a new relative distance gait feature that improves recognition accuracy and can be combined with anthropometric features for enhanced performance.

Findings

Recognition accuracy reaches up to 85% with relative distance features.

Combining relative distance and anthropometric features exceeds 95% accuracy.

Relative distance features are effective and promising for further research.

Abstract

Gait and static body measurement are important biometric technologies for passive human recognition. Many previous works argue that recognition performance based completely on the gait feature is limited. The reason for this limited performance remains unclear. This study focuses on human recognition with gait feature obtained by Kinect and shows that gait feature can effectively distinguish from different human beings through a novel representation -- relative distance-based gait features. Experimental results show that the recognition accuracy with relative distance features reaches up to 85%, which is comparable with that of anthropometric features. The combination of relative distance features and anthropometric features can provide an accuracy of more than 95%. Results indicate that the relative distance feature is quite effective and worthy of further study in more general scenarios (e.g., without Kinect).