Pose and expression recognition using limited feature points based on a dynamic bayesian network

Authors:
Wei Zhao;Goo-Rak Kwon;Sang-Woong Lee
Affiliations:
Department of Computer Engineering, Chosun University, Gwangju, Korea;Department of Information and Communication Engineering, Chosun University, Gwangju, Korea;Department of Computer Engineering, Chosun University, Gwangju, Korea
Venue:
ARES'11 Proceedings of the IFIP WG 8.4/8.9 international cross domain conference on Availability, reliability and security for business, enterprise and health information systems
Year:
2011

Citing 6
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Classifying Facial Actions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding

IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic location of facial feature points and synthesis of facial sketches using direct combined model

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Automatic and Robust Detection of Facial Features in Frontal Face Images

UKSIM '11 Proceedings of the 2011 UKSim 13th International Conference on Modelling and Simulation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In daily life, language is an important tool during the communications between people. Except the language, facial actions can also provide a lot of information. Therefore, facial actions recognition becomes a popular research topic in Human-Computer Interaction (HCI) field. However, it is always a challenging task because of its complexity. In a literal sense, there are thousands of facial muscular movements many of which have very subtle differences. Moreover, muscular movements always occur spontaneously when the pose is changed. To address this problem, firstly we build a fully automatic facial points detection system based on local Gabor filter bank and Principal Component Analysis (PCA). Then the Dynamic Bayesian networks (DBNs) are proposed to perform facial actions recognition using junction tree algorithm over a limited number of feature points. In order to evaluate the proposed method, we have applied the Korean face database for model training, and CUbiC FacePix, FEED, and our own database for testing. Experiment results clearly demonstrate the feasibility of the proposed approach.