Deep hand gesture recognition: A wavelet scattering alternative to convolutional networks

Abstract

Hand gesture recognition (HGR) is crucial for improving human-computer interaction, aiding people with disabilities, and enhancing industrial efficiency. Radars are a popular choice for HGR, as they can detect hand gestures in various lighting conditions, through obstructions, with low latency, and without line-of-sight. Deep Convolutional Neural Networks (DCNN) are commonly used to analyze radar signals and recognize complex hand gestures. However, training DCNN models requires significant computational resources, making real-time applications challenging. This study proposes using Wavelet Scattering Transform (WST) as a feature extractor to replace DCNN, while relying on lightweight traditional classifiers for identifying the class of hand movement. WST is a non-linear signal representation that preserves high levels of discriminability while maintaining stability under time-warping deformations. To compare DCNN against WST, the study used a publicly available database of ultra-wideband (UWB) impulse radar gestures, collected from eight participants performing twelve hand gestures. The results showed that WST can achieve an average accuracy of 95% across all subjects, making it a reliable, computationally efficient, and accurate alternative to DCNN. This is the first research demonstrating the effectiveness of WST against DCNN for radar HGR applications (to the best of the authors' knowledge). © 2023 IEEE.

Keywords

Deep Learning, hand gesture recognition, Wavelet Scattering

Document Type

Conference Proceeding

Date of Publication

2023

Publication Title

2023 IEEE Statistical Signal Processing Workshop (SSP)

Publisher

IEEE

School

School of Science

RAS ID

60780

Comments

Al-Jumaily, A., & Khushaba, R. N. (2023). Deep hand gesture recognition: A wavelet scattering alternative to convolutional networks. In 2023 IEEE Statistical Signal Processing Workshop (SSP) (pp. 438-442). IEEE. https://doi.org/10.1109/SSP53291.2023.10208011

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Link to publisher version (DOI)

10.1109/SSP53291.2023.10208011