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Hand Gesture Recognition using Multiclass Support Vector Machine

by Md. Hafizur Rahman, Jinia Afrin
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 1
Year of Publication: 2013
Authors: Md. Hafizur Rahman, Jinia Afrin
10.5120/12852-9367

Md. Hafizur Rahman, Jinia Afrin . Hand Gesture Recognition using Multiclass Support Vector Machine. International Journal of Computer Applications. 74, 1 ( July 2013), 39-43. DOI=10.5120/12852-9367

@article{ 10.5120/12852-9367,
author = { Md. Hafizur Rahman, Jinia Afrin },
title = { Hand Gesture Recognition using Multiclass Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 1 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 39-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number1/12852-9367/ },
doi = { 10.5120/12852-9367 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:41:06.604238+05:30
%A Md. Hafizur Rahman
%A Jinia Afrin
%T Hand Gesture Recognition using Multiclass Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 1
%P 39-43
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Vision-based recognition system has developed rapidly over the past few years. This paper presents hand gesture recognition system that can be used for interfacing between computer and human using hand gesture. In natural Human Computer Interactions (HCI), visual interpretation of gestures can be very useful. In this paper we propose a method for recognizing hand gestures using Support Vector Machine (SVM). We propose a system which can identify specific hand gestures and use them to convey information. In this system we select the feature vectors by Biorthogonal Wavelet Transform. These extracted features are used as input to the classifier. Multi Class SVM is used for classifying hand gestures into ten categories: A, B, C, D, G, H, I, L, V, Y. This system gives us good performance for recognizing the gestures. We can get up to 92% correct results on a particular gesture set.

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Index Terms

Computer Science
Information Sciences

Keywords

Gesture Recognition Canny Edge Detection Radon Transform Biorthogonal Wavelet Multiclass Support Vector Machine

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