Point-cloud-based hand gesture recognition using principal component analysis and boundary extraction

IF 4.2 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Image and Vision Computing Pub Date : 2025-06-14 DOI:10.1016/j.imavis.2025.105625

Yiwen Zhang , Dong An , Dongzhao Yang , Tianxu Xu , Yuxuan He , Qiang Wang , Zhongqi Pan , Yang Yue

引用次数: 0

Abstract

In this work, we introduce a method for hand gesture recognition that utilizes a Time-of-Flight (ToF) camera and 3D point cloud networks. A dataset of hand gesture point clouds, specifically digits 0–9, is created using a ToF depth camera. These data are then subjected to a data compression algorithm, which combines point cloud principal component analysis (PCA) with point cloud boundary extraction. The effectiveness of the proposed data compression algorithm in the context of hand gesture recognition is evaluated using seven different point cloud recognition networks. The experimental results demonstrate that the algorithm not only exhibits generalizability across various point cloud classification models but also significantly reduces the size of the hand gesture point cloud data while maintaining high recognition accuracy.

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基于主成分分析和边界提取的点云手势识别

在这项工作中，我们介绍了一种利用飞行时间（ToF）相机和3D点云网络进行手势识别的方法。使用ToF深度相机创建手势点云数据集，特别是数字0-9。然后将这些数据进行数据压缩算法，该算法将点云主成分分析（PCA）与点云边界提取相结合。采用7种不同的点云识别网络，对所提出的数据压缩算法在手势识别环境中的有效性进行了评估。实验结果表明，该算法不仅具有跨各种点云分类模型的可泛化性，而且在保持较高识别精度的同时显著减小了手势点云数据的大小。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Image and Vision Computing 工程技术-工程：电子与电气

CiteScore

8.50

自引率

8.50%

发文量

143

审稿时长

7.8 months

期刊介绍： Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.