Model-based global and local motion estimation for videoconference sequences

In this work, we present an algorithm for face 3-D motion estimation in videoconference sequences. The algorithm is able to estimate both the position of the face as an object in 3-D space (global motion) and the movements of portions of the face, like the mouth or the eyebrows ( local motion). The...

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Vydáno v:IEEE transactions on circuits and systems for video technology Ročník 14; číslo 9; s. 1156 - 1161
Hlavní autoři: Calvagno, G., Fantozzi, F., Rinaldo, R., Viareggio, A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.09.2004
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Applied sciences
Estimates
Exact sciences and technology
Eyebrows
Face detection
Frames
Image processing
Information, signal and communications theory
Motion detection
Motion estimation
Mouth
Optimization
Robustness
Shape
Signal processing
Studies
Telecommunications and information theory
Testing
Texture mapping
Tracking
Videoconference
Video coding
feature tracking
Motion estimation
Adaptive filtering
template matching
lip tracking
Algorithm
Videoconference
face tracking
Video signal processing
Block matching
Three dimensional model
model- based coding
eyebrow tracking
Feature extraction
Kalman filtering
Adaptive Kalman filtering
ISSN:1051-8215, 1558-2205
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Popis
Shrnutí:In this work, we present an algorithm for face 3-D motion estimation in videoconference sequences. The algorithm is able to estimate both the position of the face as an object in 3-D space (global motion) and the movements of portions of the face, like the mouth or the eyebrows ( local motion). The algorithm uses a modified version of the standard 3-D face model CANDIDE. We present various techniques to increase robustness of the global motion estimation which is based on feature tracking and an extended Kalman filter. Global motion estimation is used as a starting point for local motion detection in the mouth and eyebrow areas. To this purpose, synthetic images of these areas (templates) are generated with texture mapping techniques, and then compared to the corresponding regions in the current frame. A set of parameters, called action unit vectors (AUVs) influences the shape of the synthetic mouth and eyebrows. The optimal AUV values are determined via a gradient-based minimization procedure of the error energy between the templates and the actual face areas. The proposed scheme is robust and was tested with success on sequences of many hundreds of frames.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2004.833167