By Topic

Real-Time hand Gesture Recognition Using Pseudo 3-D Hidden Markov Model

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Nguyen Dang Binh ; Intelligence Media Laboratory, Kyushu Institute of Technology, 820-4, Kawazu, Iizuka, Fukuoka 820, Japan, ; Toshiaki Ejima

In the following work we present a new approach to recognition of hand gesture based on pseudo three-dimensional hidden Markov model (P3DHMM), a technique which can integrate spatial as well as temporal derived features in an elegant and efficient way. Additionally, robust and flexible hand gesture tracking using an appearance-based condensation tracker. These allow the recognition of dynamic gestures as well as more static gestures. Furthermore, there has been proposed to improve the overall performance of the approach: replace Baum-Welch algorithm with clustering algorithm, adding a clustering performance measure to the clustering algorithm and adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. Proposed improving methods along with the P3DHMM was used to develop a complete Japanese Kana hand alphabet recognition system consisting of 42 static postures and 34 hand motions. We obtained a recognition rate of 99.1% in the gesture recognition experiments when compared to P2DHMMs

Published in:

2006 5th IEEE International Conference on Cognitive Informatics  (Volume:2 )

Date of Conference:

17-19 July 2006