Hand shape classification using DTW and LCSS as similarity measures for vision-based gesture recognition system

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, han...

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Bibliographic Details
Main Authors: Kuzmanic, A., Zanchi, V.
Format: Conference Proceeding
Language:English
Subjects:
Classification algorithms
Data mining
Feature extraction
Handicapped aids
image classification
Image recognition
Image representation
Image segmentation
Shape control
Shape measurement
sign language
similarity measures
Spatial databases
Online Access:Request full text
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Summary: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.
DOI:10.1109/EURCON.2007.4400350