CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
research
Temporal Exemplar-based Bayesian Networks for facial expression recognition
Authors
KP Chan
L Shang
Publication date
1 January 2008
Publisher
'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Cite
Abstract
Proceedings of the International Conference on Machine Learning and Applications, 2008, p. 16-22We present a Temporal Exemplar-based Bayesian Networks (TEBNs) far facial expression recognition. The proposed Bayesian Networks (BNs) consists of three layers: Observation layer, Exemplars layer and Prior Knowledge layer. In the Exemplars layer, exemplar-based model is integrated with BNs to improve the accuracy of probability estimation. In the Prior Knowledge layer, static BNs is extended to Temporal BNs by considering historical observations to model temporal behavior of facial expression. Experiment on CMU expression database illustrates that the proposed TEBNs is very efficient in modeling the evolution of facial deformation. © 2008 IEEE.published_or_final_versio
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
HKU Scholars Hub
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:hub.hku.hk:10722/61208
Last time updated on 01/06/2016