Robust Interactive Method for Hand Gestures Recognition Using Machine Learning
作者机构：Department of Computer ScienceCollege of ComputerQassim UniversityBuraydah51452Saudi Arabia BIND Research GroupCollege of ComputerQassim UniversityBuraydah51452Saudi Arabia
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2022年第72卷第7期
页 面：577-595页
核心收录：
学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程]
主 题：Hand gesture recognition canny edge detector histogram of oriented gradient ensemble classifier majority voting
摘 要：The Hand Gestures Recognition(HGR)System can be employed to facilitate communication between humans and computers instead of using special input and output *** devices may complicate communication with computers especially for people with *** gestures can be defined as a natural human-to-human communication method,which also can be used in human-computer *** researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable *** aims to develop a powerful hand gesture recognition model with a 100%recognition *** proposed an ensemble classification model that combines the most powerful machine learning classifiers to obtain diversity and improve *** majority voting method was used to aggregate accuracies produced by each classifier and get the final classification *** model was trained using a self-constructed dataset containing 1600 images of ten different hand *** employing of canny’s edge detector and histogram of oriented gradient method was a great combination with the ensemble classifier and the recognition *** experimental results had shown the robustness of our proposed *** Regression and Support Vector Machine have achieved 100%*** developed model was validated using two public datasets,and the findings have proved that our model outperformed other compared studies.
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