Journal Article

Feature covariance matrix-based dynamic hand gesture recognition

Kang, Wenxiong, Wang, Zhiyong, Feng, David Dagan, Wu, Qiuxia, Fang, Linpu, Wu, Guile

2019

Overview

Over the past 2 decades, vision-based dynamic hand gesture recognition (HGR) has made significant progresses and been widely adopted in many practical applications. Although the advent of RGB-D cameras and deep learning-based methods provides more feasible solutions for HGR, it is still very challenging to satisfy the requirements of both high efficiency and accuracy for real-world HGR systems. In this paper, we propose a novel method using the feature covariance matrix for effective and efficient dynamic HGR. We extract a set of local feature vectors that represent local motion patterns to construct the feature covariance matrix efficiently, which also provides a compact representation of a dynamic hand gesture. By tracking hand keypoints in three successive frames and calculating their motion features, our method can be extended to both 2D dynamic HGR and 3D dynamic HGR. To evaluate the effectiveness of the proposed framework, we perform extensive experiments on three publicly available datasets (one 2D dataset and two 3D datasets). The experimental results demonstrate the effectiveness of our proposed method.

• Publisher: Springer London,Springer Nature B.V
• Subject: Artificial Intelligence / Computational Biology/Bioinformatics / Computational Science and Engineering / Computer Science / Covariance matrix / Data Mining and Knowledge Discovery / Datasets / Feature extraction / Feature recognition / Gesture recognition / Image Processing and Computer Vision / Machine learning / Original Article / Probability and Statistics in Computer Science