Gesture Control System: Using CNN based Hand Gesture Recognition for Touch-less Operation of Kiosk Machine
2nd International Conference on Signal and Information Processing, IConSIP 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2235187
ABSTRACT
Kiosk machines have gained good popularity among the general public as they are easy to operate and provide a good interactive interface. As a result, multiple users use the kiosk machine throughout the day to find the information they are looking for. Users interact with the kiosk machine by the means of touching its screen or using the buttons. Due to this, it is observed that throughout the day hundreds or even thousands of people end up touching the surface of the kiosk machine. Because of this hygiene cannot be maintained as it is not possible to sanitize the kiosk machine after each use. This has become a serious issue considering the effects that the Covid-19 pandemic had on the world. Multiple people touching the same surface is one of the most common ways through which the virus can spread. To help deal with this problem we have designed a gesture control system using deep learning techniques through which kiosk machines can be operated in a touch-less way. © 2022 IEEE.
Computer Vision; Convolutional Neural Network; Deep Learning; Hand Gesture Recognition; Human Computer Interaction; Image Recognition; Touchless Kiosk Machine; Transfer Learning; Computer control systems; Computer viruses; Gesture recognition; Learning systems; Palmprint recognition; Touch screens; General publics; Gesture control; Hand-gesture recognition; Interactive interfaces; Multiple user; Touchless; Convolutional neural networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Signal and Information Processing, IConSIP 2022
Year:
2022
Document Type:
Article
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