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Computers 2017, 6(2), 20; doi:10.3390/computers6020020

Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words

Department of Computer Science, Faculty of Computing and Information Technology, King AbdulAziz University, Jeddah 21499, Saudi Arabia
This paper is an extended version of our published paper: Almasre, M.A.; Al-Nuaim, H. Recognizing Arabic Sign Language gestures using depth sensors and a KSVM classifier. In Proceedings of the 8th Computer Science and Electronic Engineering Conference (CEEC 2016), Colchester, UK, 28–30 September 2016.
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Author to whom correspondence should be addressed.
Academic Editor: Laith Al-Jobouri
Received: 14 April 2017 / Revised: 30 May 2017 / Accepted: 12 June 2017 / Published: 15 June 2017
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Abstract

The objective of this research was to recognize the hand gestures of Arabic Sign Language (ArSL) words using two depth sensors. The researchers developed a model to examine 143 signs gestured by 10 users for 5 ArSL words (the dataset). The sensors captured depth images of the upper human body, from which 235 angles (features) were extracted for each joint and between each pair of bones. The dataset was divided into a training set (109 observations) and a testing set (34 observations). The support vector machine (SVM) classifier was set using different parameters on the gestured words’ dataset to produce four SVM models, with linear kernel (SVMLD and SVMLT) and radial kernel (SVMRD and SVMRT) functions. The overall identification accuracy for the corresponding words in the training set for the SVMLD, SVMLT, SVMRD, and SVMRT models was 88.92%, 88.92%, 90.88%, and 90.884%, respectively. The accuracy from the testing set for SVMLD, SVMLT, SVMRD, and SVMRT was 97.059%, 97.059%, 94.118%, and 97.059%, respectively. Therefore, since the two kernels in the models were close in performance, it is far more efficient to use the less complex model (linear kernel) set with a default parameter. View Full-Text
Keywords: depth sensor; gesture recognition; support vector machine; classification; linear; radial; SVM depth sensor; gesture recognition; support vector machine; classification; linear; radial; SVM
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Almasre, M.A.; Al-Nuaim, H. Comparison of Four SVM Classifiers Used with Depth Sensors to Recognize Arabic Sign Language Words. Computers 2017, 6, 20.

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