article

Local Binary Pattern based features for sign language recognition

Authors:

M. ŽeleznýAuthors Info & Claims

Pattern Recognition and Image Analysis, Volume 22, Issue 4

Pages 519 - 526

https://doi.org/10.1134/S1054661812040062

Published: 01 October 2012 Publication History

Abstract

In this paper we focus on appearance features particularly the Local Binary Patterns describing the manual component of Sign Language. We compare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very important for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combinations on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75% for signer dependent tests and 57.54% for signer independent tests.

References

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Cited By

Khellas KSeghir R(2023)Alabib-65: A Realistic Dataset for Algerian Sign Language RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359690922:6(1-23)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3596909
Hu Y(2018)Finger spelling recognition using depth information and support vector machineMultimedia Tools and Applications10.5555/3287850.328790377:21(29043-29057)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3287850.3287903

Local Binary Pattern based features for sign language recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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Local binary pattern based features for sign language recognition

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Published In

cover image Pattern Recognition and Image Analysis

Pattern Recognition and Image Analysis Volume 22, Issue 4

October 2012

104 pages

ISSN:1054-6618

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Copyright © Copyright © 2012 Pleiades Publishing, Ltd.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 October 2012

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Cited By

Khellas KSeghir R(2023)Alabib-65: A Realistic Dataset for Algerian Sign Language RecognitionACM Transactions on Asian and Low-Resource Language Information Processing10.1145/359690922:6(1-23)Online publication date: 10-May-2023
https://dl.acm.org/doi/10.1145/3596909
Hu Y(2018)Finger spelling recognition using depth information and support vector machineMultimedia Tools and Applications10.5555/3287850.328790377:21(29043-29057)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3287850.3287903

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