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A Joint Gesture-Identity Recognition Framework Based on 4D Millimeter-Wave Radar Sensing.

Yifan Wu¹, Li Wu¹, Taiyang Hu¹

¹School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Sensors (Basel, Switzerland)

|December 11, 2025

Summary

This study introduces a radar-based system for recognizing gestures and identities simultaneously. The novel framework achieves high accuracy, improving human-computer interaction through contactless gesture recognition.

Keywords:

feature fusion gesture recognition identification millimeter wave radar multi-task recognition

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Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor IRIS

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Area of Science:

Human-Computer Interaction
Radar Signal Processing
Biometrics

Background:

Gestures offer intuitive, contactless, and private interaction for human-computer interaction (HCI) systems.
Radar-based systems are explored for gesture and identity recognition due to their privacy-preserving nature.
Existing methods often struggle with joint recognition tasks, necessitating advanced frameworks.

Purpose of the Study:

To propose a radar-based multimodal framework for joint gesture and identity recognition.
To enhance the performance of radar-based gesture-identity recognition tasks.
To develop a method for effectively fusing multimodal radar data for recognition.

Main Methods:

A preprocessing technique involving gesture-range-based valid frame detection and clutter suppression was developed.
Multidimensional gesture features, including micro-Doppler maps (MDMs), elevation-time maps (ETMs), and azimuth-time maps (ATMs), were extracted.
A Joint Recognition Framework with Cross-Modal Attention Fusion (JRF-CMAF) using Adaptive Rectification Blocks (ARBs) was proposed for multimodal data fusion.

Main Results:

The JRF-CMAF achieved 99.76% accuracy in gesture recognition.
Identity recognition accuracy reached 97.57%.
Joint gesture and identity recognition accuracy was 96.84%, outperforming conventional methods.

Conclusions:

The proposed radar-based multimodal framework significantly enhances joint gesture and identity recognition accuracy.
The JRF-CMAF effectively leverages complementary information across modalities for superior performance.
This approach offers a promising direction for advanced contactless HCI systems.