Al Farid Fahmid, Hashim Noramiza, Abdullah Junaidi, Bhuiyan Md Roman, Shahida Mohd Isa Wan Noor, Uddin Jia, Haque Mohammad Ahsanul, Husen Mohd Nizam
Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.
Technology Studies Department, Endicott College, Woosong University, Daejeon 32820, Korea.
J Imaging. 2022 May 26;8(6):153. doi: 10.3390/jimaging8060153.
Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.
研究人员最近将注意力集中在基于视觉的手势识别上。然而，由于若干限制因素，实时实现一个有效的视觉驱动手势识别系统仍然是一项挑战。本文旨在揭示在各种相机方向下，通过视觉驱动手势识别的图像采集、图像分割与跟踪、特征提取以及手势分类阶段所面临的局限性。本文研究了2012年至2022年基于视觉的手势识别系统的相关研究。其目标是找出进展较好的领域以及仍需进一步研究的领域。我们使用特定关键词在知名在线数据库中找到了108篇文章。在本文中，我们汇集了与手势识别相关的最显著的研究成果。我们对手势识别相关研究提出了不同的类别及子类别，以在该领域创建一个有价值的资源。我们以表格形式总结并分析了这些方法。在比较了手势识别领域中类似类型的方法后，我们根据研究结果得出了结论。我们的研究还考察了基于视觉的系统在识别准确率方面对手势的识别效果。识别准确率差异很大，从68%到97%不等，平均为86.6%。所考虑的局限性包括对手势的多种文本和解释以及复杂的非刚性手部特征。与当前研究相比，本文的独特之处在于它讨论了所有类型的手势识别技术。
