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Sign Language Recognition Using Hilbert Curve Features

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Image Analysis and Recognition (ICIAR 2013)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 7950))

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Abstract

In order to design an efficient vision-based gesture recognition system, the images of the hand gestures need to be represented in an accurate way. This paper presents a new method for representing hands’ images based on the Hilbert space-filling curve. The method first segments the hand and then applies a Hilbert space-filling curve to extract a feature vector. Afterwards, a classifier, such as Support Vector Machine (SVM) and Random Forest (RF), are used to classify the gestures. The Hilbert curve representation is chosen in this work since it has proved its efficiency in representing shapes with uniform background, due to its localization-preserving property of pixels. Moreover, it is also known of being invariant to translation, scaling, and stretching. The results reveal the efficiency and suitability of the proposed approach compared to that of other works in real time.

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Authors and Affiliations

  1. Mathematics Department, Wilfrid Laurier University, 75 University Avenue, Waterloo, Ontario, Canada, N2L 3C5

    Amira Ragab, Maher Ahmed & Siu-Cheung Chau

Authors
  1. Amira Ragab
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  2. Maher Ahmed
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  3. Siu-Cheung Chau
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Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    Mohamed Kamel

  2. Faculty of Engineering, Institute of Biomedical Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    Aurélio Campilho

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Ragab, A., Ahmed, M., Chau, SC. (2013). Sign Language Recognition Using Hilbert Curve Features. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_17

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  • DOI: https://doi.org/10.1007/978-3-642-39094-4_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39093-7

  • Online ISBN: 978-3-642-39094-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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