Adaptive Gaussian Mixture Models Based Facial Actions Tracking

Recently adaptive Gaussian mixture models have become increasingly popular on account of their strong ability to adapt to variations. In this paper, an algorithm based on adaptive mixture models is proposed to track facial actions in video. WSF Mixture Appearance Model is taken to depict image obser...

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Bibliographic Details
Main Authors: Xiaoyan Wang, Yangsheng Wang, Xuetao Feng, Mingcai Zhou
Format: Conference Proceeding
Language:English
Subjects:
Active Gaussian Mixture Models
Automation
Cameras
Computer science
Convergence
Facial animation
Lighting
Robustness
Shape
Software engineering
Target tracking
tracking
Online Access:Request full text
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Summary:Recently adaptive Gaussian mixture models have become increasingly popular on account of their strong ability to adapt to variations. In this paper, an algorithm based on adaptive mixture models is proposed to track facial actions in video. WSF Mixture Appearance Model is taken to depict image observation and an active learning scheme which combines fast convergence and temporal adaptability is presented. A 3d parameterized model is used to model the face and facial actions, mixture observation model is built on shape free texture, and then a gradient descend fitting algorithm is taken to track parameters. Experiments demonstrate that the algorithm is robust and efficient.
DOI:10.1109/CSSE.2008.648