Hidden Markov models for speech recognition
Technometrics
Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Automatic Sign Language Analysis: A Survey and the Future beyond Lexical Meaning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian Process Dynamical Models for Human Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining for Genomics and Proteomics: Analyis of Gene and Protein Expression Data
Data Mining for Genomics and Proteomics: Analyis of Gene and Protein Expression Data
Inference in Hidden Markov Models
Inference in Hidden Markov Models
Thai sign language translation using Scale Invariant Feature Transform and Hidden Markov Models
Pattern Recognition Letters
| Hi-index | 0.10 |
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.