Accuracy and Efficiency Performance of the ICP Procedure Applied to Sign Language Recognition

Authors

  • Juarez Paulino da Silva Júnior
  • Marcus Vinicius Lamar
  • Jacir Luiz Bordim

DOI:

https://doi.org/10.19153/cleiej.17.2.10

Abstract

This work addresses the problem of recognizing the American Sign Language (ASL) hand alphabet relying only on depth information acquired from an RGB-D sensor. To accomplish this goal, a novel Iterative Closest Point (ICP) based recognition methodology is proposed where it comprehensively analyzes the inputs and outputs of the alignment as efficiency and accuracy determinants. Next, a novel classification technique, denoted Approximated KB-fit, is proposed to efficiently handle the space complexity of the database template matching. The overall accuracy of the recognition reached a performance of 99.04% in a cross-validation workbench with 520 distinct input depth images. The achieved frame rate was 7.41 FPS performed on a 2.4 GHz single processor based machine.

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Published

2014-08-01

Issue

Vol. 17 No. 2 (2014): CLEI 2013 special issue

Section

Articles

License

CLEIej is supported by its home institution, CLEI, and by the contribution of the Latin American and international researchers community, and it does not apply any author charges whatsoever for submitting and publishing. Since its creation in 1998, all contents are made publicly accesibly. The current license being applied is a (CC)-BY license (effective October 2015; between 2011 and 2015 a (CC)-BY-NC license was used).