Pattern Recognition and Image Analysis

, Volume 22, Issue 4, pp 519–526 | Cite as

Local Binary Pattern based features for sign language recognition

Software and Hardware for Pattern Recognition and Image Analysis

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.

Keywords

local binary pattern sign language sign language recognition 

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Copyright information

© Pleiades Publishing, Ltd. 2012

Authors and Affiliations

  1. 1.Faculty of Applied SciencesUniversity of West BohemiaPilsenCzech Republic

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