Book Chapter


Charles Markham
John McDonald
Daniel Kelly



sign language recognition detection motion natural language gestures sign language continuous video clips

Continuous recognition of motion based gestures in sign language (2009)

Abstract We present a novel and robust system for recognizing two handed motion based gestures performed within continuous sequences of sign language. While recognition of valid sign sequences is an important task in the overall goal of machine recognition of sign language, detection of movement epenthesis is important in the task of continuous recognition of natural sign language. We propose a framework for recognizing valid sign segments and identifying movement epenthesis. Our system utilizes a single HMM threshold model, per hand, to detect movement epenthesis. Further to this, we develop a novel technique to utilize the threshold model and dedicated gesture HMMs to recognize gestures within continuous sign language sentences. Experiments show that our system has a gesture detection ratio of 0.956 and a reliability measure of 0.932 when spotting 8 different signs from 240 video clips.
Collections Ireland -> Maynooth University -> Type = Book Section
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

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