Please use this identifier to cite or link to this item:
Title: 3-D hand trajectory recognition for signing exact english
Authors: Kong, W.W.
Ranganath, S. 
Issue Date: 2004
Citation: Kong, W.W., Ranganath, S. (2004). 3-D hand trajectory recognition for signing exact english. Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition : 535-540. ScholarBank@NUS Repository.
Abstract: This paper presents a hierarchical approach to recognize isolated 3-D hand gesture trajectories for Signing Exact English (SEE). SEE hand gestures can be periodic as well as non-periodic. We first differentiate between periodic and non-periodic gestures followed by recognition of individual gestures. After periodicity detection, non-periodic trajectories are classified into 8 classes and periodic trajectories are classified into 4 classes. A Polhemus tracker is used to provide the input data. Periodicity detection is based on Fourier analysis and hand trajectories are recognized by Vector Quantization Principal Component Analysis (VQPCA). The average periodicity detection accuracy is 95.9%. The average recognition rates with VQPCA for non-periodic and periodic gestures are 97.3% and 97.0% respectively. In comparison, k-means clustering yielded 87.0% and 85.1%, respectively.
Source Title: Proceedings - Sixth IEEE International Conference on Automatic Face and Gesture Recognition
ISBN: 0769521223
DOI: 10.1109/AFGR.2004.1301588
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Feb 27, 2021


checked on Feb 19, 2021

Page view(s)

checked on Feb 15, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.