A linguistic feature vector for the visual interpretation of sign language

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.