Type

Journal Article

Authors

Charles Markham
John McDonald
Daniel Kelly

Subjects

Computer Science

Topics
person recognition size sign language sign language recognition system feature recognition hand support vector machine

A person independent system for recognition of hand postures used in sign language (2010)

Abstract We present a novel user independent framework for representing and recognizing hand postures used in sign language. We propose a novel hand posture feature, an eigenspace Size Function, which is robust to classifying hand postures independent of the person performing them. An analysis of the discriminatory properties of our proposed eigenspace Size Function shows a significant improvement in performance when compared to the original unmodified Size Function. We describe our support vector machine based recognition framework which uses a combination of our eigenspace Size Function and Hu moments features to classify different hand postures. Experiments, based on two different hand posture data sets, show that our method is robust at recognizing hand postures independent of the person performing them. Our method also performs well compared to other user independent hand posture recognition systems.
Collections Ireland -> Maynooth University -> Type = Article
Ireland -> Maynooth University -> Academic Unit = Faculty of Science and Engineering
Ireland -> Maynooth University -> Status = Published
Ireland -> Maynooth University -> Open Access DRIVERset
Ireland -> Maynooth University -> Academic Unit = Faculty of Science and Engineering: Computer Science

Full list of authors on original publication

Charles Markham, John McDonald, Daniel Kelly

Experts in our system

1
Charles Markham
Maynooth University
Total Publications: 64
 
2
John McDonald
Maynooth University
Total Publications: 79
 
3
Daniel Kelly
University College Cork
Total Publications: 17