Mexican Sign Language Recognition Using Jacobi-Fourier Moments

Francisco Solís; Carina Toxqui; David Martínez

doi:10.4236/eng.2015.710061

Home > Journals > Engineering > ENG

ENG> Vol.7 No.10, October 2015

Share This Article:

Open Access

Mexican Sign Language Recognition Using Jacobi-Fourier Moments

Abstract Full-Text HTML XML

Download as PDF (Size:382KB) PP. 700-705

DOI: 10.4236/eng.2015.710061 4,169 Downloads 4,490 Views Citations

Author(s) Leave a comment

Francisco Solís¹, Carina Toxqui², David Martínez¹

Affiliation(s)

¹University Center UAEM Teotihucan Valley, Autonomous University of Mexico State, México.
²Polytechnic University of Tulancingo, Tulancingo, México.

ABSTRACT

The present work introduces a system for recognizing static signs in Mexican Sign Language (MSL) using Jacobi-Fourier Moments (JFMs) and Artificial Neural Networks (ANN). The original color images of static signs are cropped, segmented and converted to grayscale. Then to reduce computational costs 64 JFMs were calculated to represent each image. The JFMs are sorted to select a subset that improves recognition according to a metric proposed by us based on a ratio between dispersion measures. Using WEKA software to test a Multilayer-Perceptron with this subset of JFMs reached 95% of recognition rate.

KEYWORDS

Mexican Sign Language, Jacobi-Fourier Moments, Digital Image Processing

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Solís, F. , Toxqui, C. and Martínez, D. (2015) Mexican Sign Language Recognition Using Jacobi-Fourier Moments. Engineering, 7, 700-705. doi: 10.4236/eng.2015.710061.

References

[1]	Shohieb, S.M., Elminir, H.K. and Riad, A.M. (2015) SignsWorld Atlas; a Benchmark Arabic Sign Language Database. Journal of King Saud University—Computer and Information Sciences, 27, 68-76.

[2]	López, V., Barra, R., Lutfi, S., Montero, J.M. and San, R. (2013) LSESpeak: Aspoken Language Generator for Deaf People. Expert Systems with Applications, 40, 1283-1295. http://dx.doi.org/10.1016/j.eswa.2012.08.062

[3]	Solís, F., Hernández, M., Pérez, A. and Toxqui, C. (2014) Static Digits Recognition Using Rotational Signatures and Hu Moments with a Multilayer Perceptron. Engineering, 6, 692-698. http://dx.doi.org/10.4236/eng.2014.611068

[4]	Ping, Z., Ren, H., Zou, J., Sheng, Y. and Bo, W. (2007) Generic Orthogonal Moments: Jacobi-Fourier Moments for Invariant Image Description. Pattern Recognition, 40, 1245-1254. http://dx.doi.org/10.1016/j.patcog.2006.07.016

[5]	Hoang, T. and Tabbone, S. (2013) Errata and Comments on “Generic Orthogonal Moments: Jacobi-Fourier Moments for Invariant Image Description”. Pattern Recognition, 46, 3148-3155. http://dx.doi.org/10.1016/j.patcog.2013.04.011

[6]	Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. and Witten, I. (2009) The Weka Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11, 10-18. http://www.cs.waikato.ac.nz/ml/weka/ http://dx.doi.org/10.1145/1656274.1656278

[7]	Rosenblatt, F. (1962) Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. 1st Edition, Spartan Books, Michigan University, Ann Arbor.