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Dynamic Hand Gesture Recognition using Hidden Markov Model by Microsoft Kinect Sensor

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Archana Ghotkar, Pujashree Vidap, Kshitish Deo
10.5120/ijca2016911498

Archana Ghotkar, Pujashree Vidap and Kshitish Deo. Dynamic Hand Gesture Recognition using Hidden Markov Model by Microsoft Kinect Sensor. International Journal of Computer Applications 150(5):5-9, September 2016. BibTeX

@article{10.5120/ijca2016911498,
	author = {Archana Ghotkar and Pujashree Vidap and Kshitish Deo},
	title = {Dynamic Hand Gesture Recognition using Hidden Markov Model by Microsoft Kinect Sensor},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2016},
	volume = {150},
	number = {5},
	month = {Sep},
	year = {2016},
	issn = {0975-8887},
	pages = {5-9},
	numpages = {5},
	url = {http://www.ijcaonline.org/archives/volume150/number5/26087-2016911498},
	doi = {10.5120/ijca2016911498},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Hand gesture recognition is one of the leading applications of human computer interaction. With diversity of applications of hand gesture recognition, sign language interpretation is the most demanding application. In this paper, dynamic hand gesture recognition for few subset of Indian sign language recognition was considered. The use of depth camera such as Kinect sensor gave skeleton information of signer body. After detailed study of dynamic ISL vocabulary with reference to skeleton joint information, angle has identified as a feature with reference to two moving hand. Here, real time video has been captured and gesture was recognized using Hidden Markov Model (HMM). Ten state HMM model was designed and normalized angle feature of dynamic sign was being observed. Maximum likelihood probability symbol was considered as a recognized gesture. Algorithm has been tested on ISL 20 dynamic signs of total 800 training set of four persons and achieved 89.25% average accuracy.

References

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Keywords

Indian Sign Language, Dynamic hand gesture recognition, Hidden Markov Model