Exploring More from Multiple Gait Modalities for Human Identification

TL;DR

This paper conducts a comprehensive comparison of gait modalities like silhouette, human parsing, and optical flow for human identification, proposing a new fusion strategy and framework to enhance gait feature learning.

Contribution

It provides an in-depth analysis of popular gait representations and introduces C$^2$Fusion and MultiGait++ for improved gait-based human identification.

Findings

Silhouette, human parsing, and optical flow have distinct advantages and limitations.

C$^2$Fusion effectively combines multiple gait modalities for better performance.

Extensive experiments validate the superiority of the proposed framework.

Abstract

The gait, as a kind of soft biometric characteristic, can reflect the distinct walking patterns of individuals at a distance, exhibiting a promising technique for unrestrained human identification. With largely excluding gait-unrelated cues hidden in RGB videos, the silhouette and skeleton, though visually compact, have acted as two of the most prevailing gait modalities for a long time. Recently, several attempts have been made to introduce more informative data forms like human parsing and optical flow images to capture gait characteristics, along with multi-branch architectures. However, due to the inconsistency within model designs and experiment settings, we argue that a comprehensive and fair comparative study among these popular gait modalities, involving the representational capacity and fusion strategy exploration, is still lacking. From the perspectives of fine vs. coarse-grained shape and whole vs. pixel-wise motion modeling, this work presents an in-depth investigation of three popular gait representations, i.e., silhouette, human parsing, and optical flow, with various fusion evaluations, and experimentally exposes their similarities and differences. Based on the obtained insights, we further develop a C$^2$Fusion strategy, consequently building our new framework MultiGait++. C$^2$Fusion preserves commonalities while highlighting differences to enrich the learning of gait features. To verify our findings and conclusions, extensive experiments on Gait3D, GREW, CCPG, and SUSTech1K are conducted. The code is available at https://github.com/ShiqiYu/OpenGait.