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Continuous recognition of motion based gestures in sign language
Kelly, Daniel; McDonald, John; Markham, Charles
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.
Keyword(s): image recognition; feature extraction; hidden Markov models; image motion analysis
Publication Date:
Type: Book chapter
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Kelly, Daniel and McDonald, John and Markham, Charles (2009) Continuous recognition of motion based gestures in sign language. In: IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), 2009. IEEE, pp. 1073-1080. ISBN 9781424444427
Publisher(s): IEEE
File Format(s): other
Related Link(s):
First Indexed: 2020-04-02 06:30:54 Last Updated: 2020-04-02 06:30:54