Paper Info
Title
Head gesture recognition based on bayesian network
Abstract
Head gestures such as nodding and shaking are often used as one of human body languages for communication with each other, and their recognition plays an important role in the development of Human-Computer Interaction (HCI). As head gesture is the continuous motion on the sequential time series, the key problems of recognition are to track multi-view head and understand the head pose transformation. This paper presents a Bayesian network (BN) based framework, into which multi-view model (MVM) and the head gesture statistic inference model are integrated for recognizing. Finally the decision of head gesture is made by comparing the maximum posterior, the output of BN, with some threshold. Additionally, in order to enhance the robustness of our system, we add the color information into BN in a new way. The experimental results illustrate that the proposed algorithm is effective.
Year
DOI
Venue
2005
10.1007/11492429_60
IbPRIA (1)
Keywords
Field
DocType
color information,bayesian network,human-computer interaction,multi-view head,continuous motion,human body language,multi-view model,head gesture,head gesture recognition,head gesture statistic inference,human body,gesture recognition,time series,human computer interaction,statistical inference
Gesture,Computer science,Image processing,Gesture recognition,Robustness (computer science),Artificial intelligence,Active shape model,Computer vision,Pattern recognition,Inference,Speech recognition,Bayesian network,User interface
Conference
Volume
ISSN
ISBN
3522
0302-9743
3-540-26153-2
Citations 
PageRank 
References 
3
0.46
7
Authors
4
Name
Order
Citations
PageRank
Peng Lu112617.62
Xiangsheng Huang222320.96
Xinshan Zhu3428.12
Yangsheng Wang475066.25