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    A person independent system for recognition of hand postures used in sign language


    Kelly, Daniel and McDonald, John and Markham, Charles (2010) A person independent system for recognition of hand postures used in sign language. Pattern Recognition Letters, 31 (11). pp. 1359-1368. ISSN 0167-8655

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    Abstract

    We present a novel user independent framework for representing and recognizing hand postures used in sign language. We propose a novel hand posture feature, an eigenspace Size Function, which is robust to classifying hand postures independent of the person performing them. An analysis of the discriminatory properties of our proposed eigenspace Size Function shows a significant improvement in performance when compared to the original unmodified Size Function. We describe our support vector machine based recognition framework which uses a combination of our eigenspace Size Function and Hu moments features to classify different hand postures. Experiments, based on two different hand posture data sets, show that our method is robust at recognizing hand postures independent of the person performing them. Our method also performs well compared to other user independent hand posture recognition systems.

    Item Type: Article
    Keywords: Feature representation; Moments; Size and shape; Object recognition; Tracking; Classifier design and evaluation;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8289
    Identification Number: https://doi.org/10.1016/j.patrec.2010.02.004
    Depositing User: John McDonald
    Date Deposited: 08 Jun 2017 14:41
    Journal or Publication Title: Pattern Recognition Letters
    Publisher: Elsevier
    Refereed: Yes
    URI:

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