Self-directed-learning for sign language recognition

Authors:
Huaqiang Jiang;Huosheng Hu
Affiliations:
College of Mechatronics Engineering and Automation, Shanghai University, Shanghai, China and College of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China;School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK
Venue:
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
Year:
2009

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Abstract

This paper proposes a multi-classified and self-directed learning method used for sign language recognition, which adopts statistical template matching methods to recognize sign language. As sign language expressions consist of many frames, SIFT algorithm is used to position key frames and eigenvectors of sign language vocabulary. According to these key frames, the hierarchical discriminate regression (HDR) method is adopted to narrow the searching scope. Then, these obtained features are compared and matched with every words of sign language in the dynamic time warping (DTW) scope. The recognition rate of this method is 85%, which is higher than HMM under the same condition. This could greatly speed up the construction process of a sign language database.