Share Email Print

Proceedings Paper

Dynamic gesture recognition using PCA with multiscale theory and HMM
Author(s): Hai Wu; Alistair Sutherland
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

In this paper, a dynamic gesture recognition system is presented which requires no special hardware other than a Webcam. The system is based on a novel method combining Principal Component Analysis (PCA) with hierarchical multi-scale theory and Discrete Hidden Markov Models (DHMM). We use a hierarchical decision tree based on multiscale theory. Firstly we convolve all members of the training data with a Gaussian kernel, which blurs differences between images and reduces their separation in feature space. This reduces the number of eigenvectors needed to describe the data. A principal component space is computed from the convolved data. We divide the data in this space into two clusters using the k-means algorithm. Then the level of blurring is reduced and PCA is applied to each of the clusters separately. A new principal component space is formed from each cluster. Each of these spaces is then divided into two and the process is repeated. We thus produce a binary tree of principal component spaces where each level of the tree represents a different degree of blurring. The search time is then proportional to the depth of the tree, which makes it possible to search hundreds of gestures in real time. The output of the decision tree is then input into DHMM to recognize temporal information.

Paper Details

Date Published: 21 September 2001
PDF: 8 pages
Proc. SPIE 4550, Image Extraction, Segmentation, and Recognition, (21 September 2001); doi: 10.1117/12.441451
Show Author Affiliations
Hai Wu, Dublin City Univ. (Ireland)
Alistair Sutherland, Dublin City Univ. (Ireland)

Published in SPIE Proceedings Vol. 4550:
Image Extraction, Segmentation, and Recognition

© SPIE. Terms of Use
Back to Top