Abstract

This paper presents a novel approach to sign language recog- nition that provides extremely high classification rates on minimal train- ing 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 lin- guistics and describes actions at a conceptual level easily understood by humans. Moreover, such a description broadly generalises temporal activ- ities naturally overcoming variability of people and environments. A sec- ond 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 in- stance training outperforming previous approaches where thousands of training examples are required.