Local binary pattern based features for sign language recognition

In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. 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 importa...

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Bibliographic Details
Published in:Pattern recognition and image analysis 2011-09, Vol.21 (3), p.398-401
Main Authors: Hruz, M, Trojanova, J, elezny, M
Format: Article
Language:English
Subjects:
Computer Science
Computer vision
Dictionaries
Experiments
Face recognition
Feature recognition
Image Processing and Computer Vision
Landmarks
Localization
Mathematical models
Pattern Recognition
Pattern recognition systems
Recognition
Robustness
Sign language
Software and Hardware for Pattern Recognition and Image Analysis
Studies
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Summary:In this paper we focus on appearance features describing the manual component of Sign Language particularly the Local Binary Patterns. 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.
ISSN:1054-6618
1555-6212
DOI:10.1134/S1054661811020416