Gaurav Chaturvedi
, Himayanth V
Hand Gesture, MediaPipe, Gaussian regression, Hybrid classification, Feature extraction
The area of gesture recognition is changing due to the development of new technology. With the introduction of technologies like the Kinect sensor, there are now more potential for human-computer interaction than with the conventional hand gesture recognition approaches, which are more dependent on gloves and sensors and less adaptable. The aim of working with picture datasets for improved recognition is to extract additional features to improve gesture recognition. It has been noted that adding more characteristics makes it much simpler to accurately recognise hand gestures, and accuracy may also be raised by optimizing the classification process. To improve the performance of the gesture recognition model, enhanced feature extraction and hybrid classification are used in this study.
"Hand Gesture Recognition ", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.7, Issue 11, page no.320 - 324, November-2022, Available :https://ijsdr.org/papers/IJSDR2211052.pdf
Volume 7
Issue 11,
November-2022
Pages : 320 - 324
Paper Reg. ID: IJSDR_202528
Published Paper Id: IJSDR2211052
Downloads: 000347197
Research Area: Computer Science & Technology
Country: Kanchipuram, Tamil Nadu, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
