Gaussian Process Dynamical Models for hand gesture interpretation in Sign Language

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.

► Introduction of Gaussian Process Dynamical Model (GPDM) for Sign Language gesture recognition.
► Testing GPDM with a database of 66 gestures of Malaysian Sign Language (MSL) and achieving the classification accuracy of 79%.
► Comparison of GPDM against Hidden Markov Model (HMM) for gesture recognition.