JEDAN: Joint Euclidean Distance and Autoencoder Network for Robust Out-of-Distribution Detection in Radar-Based Hand Gesture Recognition

JEDAN: Joint Euclidean Distance and Autoencoder Network for Robust Out-of-Distribution Detection in Radar-Based Hand Gesture Recognition

Detecting Out-of-Distribution (OOD) gestures is vital for reliable radar-based gesture-recognition systems. Traditional autoencoders often fall short in OOD detection because they prioritize minimizing reconstruction error over forming distinct clusters in the latent space. This study proposes Joint...

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Bibliographic Details
Main Authors: Muhammad Ghufran Janjua, Kevin Kaiser, Thomas Stadelmayer, Stephan Schoenfeldt, Vadim Issakov
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Artificial neural networks
autoencoders
deep learning
FMCW radar
gesture recognition
out-of-distribution detection
Online Access:https://ieeexplore.ieee.org/document/10810384/
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https://ieeexplore.ieee.org/document/10810384/

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