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HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST

Abstract: 

In this paper, we propose a method for automatic hand gesture recognition using a random regression forest with a novel set of feature descriptors created from skeletal data acquired from the Leap Motion Controller. The efficacy of our proposed approach is evaluated on the publicly available University of Padova Microsoft Kinect and Leap Motion dataset, as well as 24 letters of the English alphabet in American Sign Language. The letters that are dynamic (e.g. j and z) are not evaluated. Using a random regression forest to classify the features we achieve 100% accuracy on the University of Padova Microsoft Kinect and Leap Motion dataset. We also constructed an in-house dataset using the 24 static letters of the English alphabet in ASL. A classification rate of 98.36% was achieved on this dataset. We also show that our proposed method outperforms the current state of the art on the University of Padova Microsoft Kinect and Leap Motion dataset.

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Paper Details

Authors:
Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin
Submitted On:
19 September 2017 - 8:27pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Shaun Canavan
Paper Code:
MA-PG.6
Document Year:
2017
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HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST

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[1] Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin, "HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2236. Accessed: Oct. 23, 2017.
@article{2236-17,
url = {http://sigport.org/2236},
author = {Shaun Canavan; Walter Keyes; Ryan McCormick; Julie Kunnumpurath; Tanner Hoelzel; Lijun Yin },
publisher = {IEEE SigPort},
title = {HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST},
year = {2017} }
TY - EJOUR
T1 - HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST
AU - Shaun Canavan; Walter Keyes; Ryan McCormick; Julie Kunnumpurath; Tanner Hoelzel; Lijun Yin
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2236
ER -
Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin. (2017). HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST. IEEE SigPort. http://sigport.org/2236
Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin, 2017. HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST. Available at: http://sigport.org/2236.
Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin. (2017). "HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST." Web.
1. Shaun Canavan, Walter Keyes, Ryan McCormick, Julie Kunnumpurath, Tanner Hoelzel, Lijun Yin. HAND GESTURE RECOGNITION USING A SKELETON-BASED FEATURE REPRESENTATION WITH A RANDOM REGRESSION FOREST [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2236