# Hybrid Skeleton-Based Motion Templates for Cross-View and Appearance-Robust Gait Recognition

## TL;DR

This paper introduces a new gait recognition method using skeleton-based motion templates that are more robust to changes in appearance and viewpoint compared to traditional methods.

## Contribution

The novel contribution is the development of Gait Skeleton Images (GSIs), compact 2D skeletal descriptors that improve gait recognition under varying conditions.

## Key findings

- GSI variants show reduced performance degradation under appearance changes compared to GEIs.
- GSIs demonstrate greater stability under severe cross-view conditions.
- GSIs complement appearance-based descriptors and benefit from advances in 3D pose estimation.

## Abstract

Gait recognition methods based on silhouette templates, such as the Gait Energy Image (GEI), achieve high accuracy under controlled conditions but often degrade when appearance varies due to viewpoint, clothing, or carried objects. In contrast, skeleton-based approaches provide interpretable motion cues but remain sensitive to pose-estimation noise. This work proposes two compact 2D skeletal descriptors—Gait Skeleton Images (GSIs)—that encode 3D joint trajectories into line-based and joint-based static templates compatible with standard 2D CNN architectures. A unified processing pipeline is introduced, including skeletal topology normalization, rigid view alignment, orthographic projection, and pixel-level rendering. Core design factors are analyzed on the GRIDDS dataset, where depth-based 3D coordinates provide stable ground truth for evaluating structural choices and rendering parameters. An extensive evaluation is then conducted on the widely used CASIA-B dataset, using 3D coordinates estimated via human pose estimation, to assess robustness under viewpoint, clothing, and carrying covariates. Results show that although GEIs achieve the highest same-view accuracy, GSI variants exhibit reduced degradation under appearance changes and demonstrate greater stability under severe cross-view conditions. These findings indicate that compact skeletal templates can complement appearance-based descriptors and may benefit further from continued advances in 3D human pose estimation.