Sciweavers

Share
ECCV
2004
Springer

A Linguistic Feature Vector for the Visual Interpretation of Sign Language

9 years 10 months ago
A Linguistic Feature Vector for the Visual Interpretation of Sign Language
Abstract. This paper presents a novel approach to sign language recognition that provides extremely high classification rates on minimal training data. Key to this approach is a 2 stage classification procedure where an initial classification stage extracts a high level description of hand shape and motion. This high level description is based upon sign linguistics and describes actions at a conceptual level easily understood by humans. Moreover, such a description broadly generalises temporal activities naturally overcoming variability of people and environments. A second stage of classification is then used to model the temporal transitions of individual signs using a classifier bank of Markov chains combined with Independent Component Analysis. We demonstrate classification rates as high as 97.67% for a lexicon of 43 words using only single instance training outperforming previous approaches where thousands of training examples are required.
Richard Bowden, David Windridge, Timor Kadir, Andr
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2004
Where ECCV
Authors Richard Bowden, David Windridge, Timor Kadir, Andrew Zisserman, Michael Brady
Comments (0)
books