A Novel Machine Learning based Multiple-user Hand Gesture Recognition Approach
Abstract
Machines are generally designed offering attributes, such as, of usability, accuracy, affordability, and scalability. This study aims to facilitate value addition Human-Computer Interaction employing hand gesture recognition, introducing a user-friendly and natural interaction for conveying useful information. Initially, machine vision-based techniques are applied for hand segmentation and detection, subsequently, deep learning classifier is trained on various hand gestures. In this study, shape-based hand features, which offer less variation under various lighting conditions are presented in contrast to the other means of hand gesture recognition (such as texture and skin colour). Furthermore, experiments were conducted under varying light conditions and the recognition performance of the developed algorithm for multiple-user hand gesture is investigated. The developed method results in achieving a classification accuracy of 94.6% and 93.% for single and multiple-user hand gestures respectively. The developed hand gesture-based non-verbal communication would assist handicap and physically challenged personals for non-invasive machine interaction.
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