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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(6): 750-753 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.229

Sign Language Recognition Using Motion History Volume and Hybrid Neural Networks

Ho-Joon Kim, So-Jeong Park, and Seung-Kang Lee
Abstract—In this paper, we present a sign language recognition model which does not use any wearable devices for object tracking. The system design issues and implementation issues such as data representation, feature extraction and pattern classification methods are discussed. The proposed data representation method for sign language patterns is robust for spatio-temporal variances of feature points. We present a feature extraction technique which can improve the computation speed by reducing the amount of feature data. A neural network model which is capable of incremental learning is introduced. We have defined a measure which reflects the relevance between the feature types and the pattern classes. The measure makes it possible to select more effective features without any degradation of performance. Through the experiments using six types of sign language patterns, the proposed model is evaluated empirically.

Index Terms—Sign language recognition, neural network, feature extraction, pattern classification.

Ho-Joon Kim is with the School of Computer Science and Electric Engineering, Handong Global University, Pohang, Kyungbuk, Korea (e-mail: hjkim@handong.edu).
So-Jeong Park and Seung-Kang Lee are with the Dept. of Information and Communication at Handong Global University, Pohang, Kyungbuk, Korea (email: sojeongmon@gmail.com; lsk0704@gmail.com)

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Cite:Ho-Joon Kim, So-Jeong Park, and Seung-Kang Lee, "Sign Language Recognition Using Motion History Volume and Hybrid Neural Networks," International Journal of Machine Learning and Computing vol.2, no. 6, pp. 750-753, 2012.

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