Title :
Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system
Author :
Ana Kuzmanic;Vlasta Zanchi
Author_Institution :
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture/Laboratory for Biomechanics and Automatic Control Systems, University of Split, Split, Croatia e-mail: akuzmani@fesb.hr
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 :
"Shape measurement","Handicapped aids","Feature extraction","Image recognition","Data mining","Image representation","Image segmentation","Shape control","Spatial databases","Classification algorithms"
Conference_Titel :
EUROCON, 2007. The International Conference on "Computer as a Tool"
Print_ISBN :
978-1-4244-0812-2
DOI :
10.1109/EURCON.2007.4400350