An Experimental Analysis of Various Machine Learning Algorithms for Hand Gesture Recognition

Author First name, Last name, Institution

Shashi Bhushan, University of Petroleum and Energy Studies
Mohammed Alshehri, Majmaah University
Ismail Keshta, Almaarefa University
Ashish Kumar Chakraverti, Sharda University
Jitendra Rajpurohit, University of Petroleum and Energy Studies
Ahed Abugabah, Zayed UniversityFollow

Document Type

Article

Source of Publication

Electronics (Switzerland)

Publication Date

3-1-2022

Abstract

Nowadays, hand gestures have become a booming area for researchers to work on. In communication, hand gestures play an important role so that humans can communicate through this. So, for accurate communication, it is necessary to capture the real meaning behind any hand gesture so that an appropriate response can be sent back. The correct prediction of gestures is a priority for meaningful communication, which will also enhance human–computer interactions. So, there are several techniques, classifiers, and methods available to improve this gesture recognition. In this research, analysis was conducted on some of the most popular classification techniques such as Naïve Bayes, K-Nearest Neighbor (KNN), random forest, XGBoost, Support vector classifier (SVC), logistic regression, Stochastic Gradient Descent Classifier (SGDC), and Convolution Neural Networks (CNN). By performing an analysis and comparative study on classifiers for gesture recognition, we found that the sign language MNIST dataset and random forest outperform traditional machine-learning classifiers, such as SVC, SGDC, KNN, Naïve Bayes, XG Boost, and logistic regression, predicting more accurate results. Still, the best results were obtained by the CNN algorithm.

DOI Link

10.3390/electronics11060968

ISSN