5523956
doi
10.35940/ijeat.D2343.0410421
oai:zenodo.org:5523956
Blue Eyes Intelligence Engineering and Sciences Publication(BEIESP)
Publisher
K. Gopakumar
Professor, TKM College of Engineering, Kollam, Kerala, India
Extracting Multiple Features for Dynamic Hand Gesture Recognition
Suni S. S
Research Scholar, LBS Centre for Science and Technology, University of Kerala, Thiruvananthapuram, Kerala, India.
issn:2249-8958
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Hand gesture recognition, Histogram of orientation of optical flow, local binary pattern, Multiclass support vector machine, Scale invariant feature.
<p>In this work a framework based on histogram of orientation of optical flow (HOOF) and local binary pattern from three orthogonal planes (LBP_TOP) is proposed for recognizing dynamic hand gestures. HOOF algorithm extracts local shape and dynamic motion information of gestures from image sequences and local descriptor LBP is extended to three orthogonal planes to create an efficient motion descriptor. These features are invariant to scale, translation, illumination and direction of motion. The performance of the new framework is tested in two different ways. The first one is by fusing the global and local features as one descriptor and the other is using features separately to train the multi class support vector machine. Performance analysis shows that the proposed approach produces better results for recognizing dynamic hand gestures when compared with state of the art methods.</p>
Zenodo
2021-04-30
info:eu-repo/semantics/article
5523955
1632404915.650355
874183
md5:f470ac6e3ee75ed7927f5803db0dd0fe
https://zenodo.org/records/5523956/files/D23430410421.pdf
public
2249-8958
Is cited by
issn
International Journal of Engineering and Advanced Technology (IJEAT)
10
4
71-75
2021-04-30