Gaussian Process Dynamical Models for Hand Gesture Interpretation in Sign Language

Gamage, Nuwan and Chow, Kuang Ye and Akmeliawati, Rini and Demidenko, Serge (2011) Gaussian Process Dynamical Models for Hand Gesture Interpretation in Sign Language. Pattern Recognition Letters, 32 (15). pp. 2009-2014. ISSN 01678655

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Abstract

Classifying human hand gestures in the context of a Sign Language has been historically dominated by Artificial Neural Networks and Hidden Markov Model with varying degrees of success. The main objective of this paper is to introduce Gaussian Process Dynamical Model as an alternative machine learning method for hand gesture interpretation in Sign Language. In support of this proposition, the paper presents the experimental results for Gaussian Process Dynamical Model against a database of 66 hand gestures from the Malaysian Sign Language. Furthermore, the Gaussian Process Dynamical Model is tested against established Hidden Markov Model for a comparative evaluation. A discussion on why Gaussian Process Dynamical Model is superior over existing methods in Sign Language interpretation task is then presented.

Item Type:	Article (Journal)
Additional Information:	5806/6002
Uncontrolled Keywords:	Gesture interpretation; Gaussian Process Dynamical Model; Gaussian Process; Sign Language; Hidden Markov Model
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Prof. Dr. Rini Akmeliawati
Date Deposited:	18 Nov 2011 14:35
Last Modified:	02 Jul 2013 09:44
URI:	http://irep.iium.edu.my/id/eprint/6002

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