Please use this identifier to cite or link to this item: http://nopr.niscpr.res.in/handle/123456789/61653
metadata.dc.identifier.doi: https://doi.org/10.56042/jsir.v82i04.72387
Title: Gesture Recognition for Enhancing Human Computer Interaction
Authors: Chakravarthi, Sangapu Sreenivasa
Rao, B Narendra Kumar
Challa, Nagendra Panini
Ranjana, R
Rai, Ankush
Keywords: Extreme learning;Finger tracking;Hand gesture;Motion detection;Voice commands
Issue Date: Apr-2023
Publisher: NIScPR-CSIR,India
Abstract: Gesture recognition is critical in human-computer communication. As observed, a plethora of current technological developments are in the works, including biometric authentication, which we see all the time in our smartphones. Hand gesture focus, a frequent human-computer interface in which we manage our devices by presenting our hands in front of a webcam, can benefit people of different backgrounds. Some of the efforts in human-computer interface include voice assistance and virtual mouse implementation with voice commands, fingertip recognition and hand motion tracking based on an image in a live video. Human Computer Interaction (HCI), particularly vision-based gesture and object recognition, is becoming increasingly important. Hence, we focused to design and develop a system for monitoring fingers using extreme learning-based hand gesture recognition techniques. Extreme learning helps in quickly interpreting the hand gestures with improved accuracy which would be a highly useful in the domains like healthcare, financial transactions and global business
Page(s): 438-443
ISSN: 0022-4456 (Print); 0975-1084 (Online)
Appears in Collections:JSIR Vol.82(04) [April 2023]

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