Novel boosting framework for subunit-based sign language recognition

George Awad, Junwei Han, Alistair Sutherland

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    17 Citations (Scopus)


    Recently, a promising research direction has emerged in sign language recognition (SLR) aimed at breaking up signs into manageable subunits. This paper presents a novel SL learning technique based on boosted subunits. Three main contributions distinguish the proposed work from traditional approaches: 1) A novel boosting framework is developed to recognize SL. The learning is based on subunits instead of the whole sign, which is more scalable for the recognition task. 2) Feature selection is performed to learn a small set of discriminative combinations of subunits and SL features. 3) A joint learning strategy is adopted to share subunits across sign classes, which leads to a better performance classifiers. Our experiments show that compared to Dynamic Time Warping (DTW) when applied on the whole sign, our proposed technique gives better results.

    Original languageEnglish
    Title of host publicationImage Processing (ICIP), 2009 16th IEEE international conference on
    Place of PublicationPiscataway, NJ
    PublisherIEEE Computer Society
    Number of pages4
    ISBN (Print)9781424456536
    Publication statusPublished - 2009
    Event16th IEEE International Conference on Image Processing - Cairo, Egypt
    Duration: 7 Nov 200910 Nov 2009


    Conference16th IEEE International Conference on Image Processing


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