# Glidar3DJ: A View-Invariant gait identification via flash lidar data correction

## TL;DR

This paper introduces Glidar3DJ, a novel view-invariant gait recognition method using skeleton data from flash lidar, with a filtering technique to handle low-resolution noise, enhancing real-world surveillance applications.

## Contribution

First model-based gait recognition approach using flash lidar data with a rule-based filtering to correct noisy skeleton measurements.

## Key findings

- Improved gait recognition accuracy with lidar skeleton data.
- Effective noise correction enhances robustness in real-world scenarios.
- Demonstrated viability of lidar-based gait recognition outside laboratory settings.

## Abstract

Gait recognition is a leading remote-based identification method, suitable for real-world surveillance and medical applications. Model-based gait recognition methods have been particularly recognized due to their scale and view-invariant properties. We present the first model-based gait recognition methodology, $\mathcal{G}$lidar3DJ using a skeleton model extracted from sequences generated by a single flash lidar camera. Existing successful model-based approaches take advantage of high quality skeleton data collected by Kinect and Mocap, for example, are not practicable for applications outside the laboratory. The low resolution and noisy imaging process of lidar negatively affects the performance of state-of-the-art skeleton-based systems, generating a significant number of outlier skeletons. We propose a rule-based filtering mechanism that adopts robust statistics to correct for skeleton joint measurements. Quantitative measurements validate the efficacy of the proposed method in improving gait recognition.