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Enhanced Gesture Recognition Performance through Improved Pre-Processing

by Jacob Grosek, Peizhe Shi, J. Nathan Kutz
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 9
Year of Publication: 2013
Authors: Jacob Grosek, Peizhe Shi, J. Nathan Kutz
10.5120/10105-4757

Jacob Grosek, Peizhe Shi, J. Nathan Kutz . Enhanced Gesture Recognition Performance through Improved Pre-Processing. International Journal of Computer Applications. 62, 9 ( January 2013), 1-8. DOI=10.5120/10105-4757

@article{ 10.5120/10105-4757,
author = { Jacob Grosek, Peizhe Shi, J. Nathan Kutz },
title = { Enhanced Gesture Recognition Performance through Improved Pre-Processing },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 9 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number9/10105-4757/ },
doi = { 10.5120/10105-4757 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:11:18.290188+05:30
%A Jacob Grosek
%A Peizhe Shi
%A J. Nathan Kutz
%T Enhanced Gesture Recognition Performance through Improved Pre-Processing
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 9
%P 1-8
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Gesture recognition is analyzed on a set of static hand gestures in the context of designing robust, real-time pre-processing techniques for applications in hand-held electronics. A comparative case study that uses various combinations of algorithms across the steps of the recognition process is made, revealing the fact that many method combinations can produce highly accurate results, even at low resolutions, given the right kind of pre-processing. The pre-processing includes the hand segmentation and normalization done before feature extraction. Indeed, pre-processing has by far the greatest effect on the overall accuracy, robustness, and speed of the gesture recognition process, significantly outweighing the influence of feature-extraction and classification. Even at image resolutions as low as 8 8 pixels, accuracies of 99% are achieved using a simple PCA feature selection scheme and a LDA classification method. These results suggest the priority and advantages of focusing on developing robust and efficient pre-processing methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Pre-processing Hand Gesture Recognition

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