Adaptive gesture recognition combining HMM models and geometrical features

Pu Cheng; Jie Zhou

doi:10.1117/12.901204

2 December 2011 Adaptive gesture recognition combining HMM models and geometrical features

Pu Cheng, Jie Zhou

Proceedings Volume 8004, MIPPR 2011: Pattern Recognition and Computer Vision; 80040J (2011) https://doi.org/10.1117/12.901204
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

Abstract

Hand gesture recognition is receiving more and more attentions due to its potential use in many applications. In this paper, we propose a novel gesture spotting and recognition method, which combines the information of hand motion parameter, the matching result of HMM models and the recognition result based on geometrical features of hand trajectory to spot and recognize the gesture. Besides, we also study the method of adjusting classifiers to make the gesture recognition system adapt to specific users. Experimental results have proved the effectiveness of the proposed method.

Citation Download Citation

Pu Cheng and Jie Zhou "Adaptive gesture recognition combining HMM models and geometrical features", Proc. SPIE 8004, MIPPR 2011: Pattern Recognition and Computer Vision, 80040J (2 December 2011); https://doi.org/10.1117/12.901204

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available