A linguistic feature vector for the visual interpretation of sign language

Conference item

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.

Authors: Bowden, R; Windridge, D; Kadir, T; Zisserman, A; Brady, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Host title: Computer Vision - ECCV 2004
• Volume: 1
• Pages: 390-401
• Series: Lecture Notes in Computer Science
• Series number: 3021
• Publication date: 2004-01-01
• Event title: 8th European Conference on Computer Vision (ECCV 2004)
• Event location: Prague, Czech Republic
• Event website: https://cmp.felk.cvut.cz/eccv2004/
• Event start date: 2014-05-11
• Event end date: 2014-05-14
• DOI: 10.1007/978-3-540-24670-1_30
• EISSN: 1611-3349
• ISSN: 0302-9743
• EISBN: 9783540246701
• ISBN: 9783540219842
• Language: English
• Keywords: bioengineering