Research Area:  Machine Learning

Abstract:

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.

Keywords:  
gesture recognition
feature extraction
gesture classification
recognition accuracy
deep learning

Author(s) Name:  Fahmid Al Farid,Noramiza Hashim ,Junaidi Abdullah,Md Roman Bhuiyan 1,*ORCID,Wan Noor Shahida Mohd Isa 1ORCID,Jia Uddin,Mohammad Ahsanul Haque and Mohd Nizam Husen

Publisher name:  MDPI

DOI:  10.3390/jimaging8060153

Volume Information:  Volume 8 , Issue 6

Paper Link:   https://www.mdpi.com/2313-433X/8/6/153