Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system
Authors:
- 1 January 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 264-269
Abstract
In this paper an approach to classify hand shapes into different classes according to the similarity measures between features is proposed. We show how to use an Exploratory Data Analysis to extract novel, single feature of hand from images. Based on the obtained curve-like shape of the feature, hands are classified into one of 21 possible classes of Croatian sign language using Dynamic Time Warping and Longest Common Subsequence as similarity measures. Performance of the system was evaluated with 1260 images. Results show that high classification accuracy can be obtained from a single feature recognition and a small number of training sample.Keywords
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