Open-Set Gait Recognition From Sparse mmWave Radar Point Clouds

The adoption of millimeter-wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of open...

Full description

Saved in:
Bibliographic Details
Published in:IEEE sensors journal Vol. 25; no. 17; pp. 33051 - 33063
Main Authors: Mazzieri, Riccardo, Pegoraro, Jacopo, Rossi, Michele
Format: Journal Article
Language:English
Published: New York IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Deep learning
Edge computing
Feature extraction
Gait
Gait recognition
Image reconstruction
Inference
Inference algorithms
Legged locomotion
Millimeter wave communication
Millimeter waves
millimeter-wave (mmWave) radar
Neural networks
open set classification
point cloud
Point cloud compression
Radar
Radar equipment
Training
ISSN:1530-437X, 1558-1748
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The adoption of millimeter-wave (mmWave) radar devices for human sensing, particularly gait recognition, has recently gathered significant attention due to their efficiency, resilience to environmental conditions, and privacy-preserving nature. In this work, we tackle the challenging problem of open-set gait recognition (OSGR) from sparse mmWave radar point clouds. Unlike most existing research, which assumes a closed-set scenario, our work considers the more realistic open-set case, where unknown subjects might be present at inference time, and should be correctly recognized by the system. Point clouds are well-suited for edge computing applications with resource constraints, but are more significantly affected by noise and random fluctuations than other representations, like the more common micro-Doppler signature. This is the first work addressing open-set gait recognition with sparse point cloud data. To do so, we propose a novel neural network architecture that combines supervised classification with unsupervised reconstruction of the point clouds, creating a robust, rich, and highly regularized latent space of gait features. To detect unknown subjects at inference time, we introduce a probabilistic novelty detection algorithm that leverages the structured latent space and offers a tunable trade-off between inference speed and prediction accuracy. Along with this article, we release mmGait10 , an original human gait dataset featuring over 2 h of measurements from ten subjects, under varied walking modalities. Extensive experimental results show that our solution attains <inline-formula> <tex-math notation="LaTeX">{24}\% </tex-math></inline-formula> average <inline-formula> <tex-math notation="LaTeX">{F}1 </tex-math></inline-formula>-score improvement over state-of-the-art methods adapted for point clouds, across multiple openness levels.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2025.3587503