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Arabic Sign Language Recognition Using Spatio-Temporal Local Binary Patterns and Support Vector Machine

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 488))

Abstract

One of the most common ways of communication in deaf community is sign language recognition. This paper focuses on the problem of recognizing Arabic sign language at word level used by the community of deaf people. The proposed system is based on the combination of Spatio-Temporal local binary pattern (STLBP) feature extraction technique and support vector machine (SVM) classifier. The system takes a sequence of sign images or a video stream as input, and localize head and hands using IHLS color space and random forest classifier. A feature vector is extracted from the segmented images using local binary pattern on three orthogonal planes (LBP-TOP) algorithm which jointly extracts the appearance and motion features of gestures. The obtained feature vector is classified using support vector machine classifier. The proposed method does not require that signers wear gloves or any other marker devices. Experimental results using Arabic sign language (ArSL) database contains 23 signs (words) recorded by 3 signers show the effectiveness of the proposed method. For signer dependent test, the proposed system based on LBP-TOP and SVM achieves an overall recognition rate reaching up to 99.5%.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan, Egypt

    Saleh Aly & Safaa Mohammed

Authors
  1. Saleh Aly
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  2. Safaa Mohammed
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Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Benha University, Benha, Egypt

    Ahmad Taher Azar

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© 2014 Springer International Publishing Switzerland

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Aly, S., Mohammed, S. (2014). Arabic Sign Language Recognition Using Spatio-Temporal Local Binary Patterns and Support Vector Machine. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-13461-1_5

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13460-4

  • Online ISBN: 978-3-319-13461-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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