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] |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| JSIR 82(04) 438-443.pdf | 1.09 MB | Adobe PDF | View/Open |
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