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ICCV
2009
IEEE

Robust facial feature tracking using selected multi-resolution linear predictors

8 years 4 months ago
Robust facial feature tracking using selected multi-resolution linear predictors
This paper proposes a learnt data-driven approach for accurate, real-time tracking of facial features using only intensity information. Constraints such as a-priori shape models or temporal models for dynamics are not required or used. Tracking facial features simply becomes the independent tracking of a set of points on the face. This allows us to cope with facial configurations not present in the training data. Tracking is achieved via linear predictors which provide a fast and effective method for mapping pixel-level information to tracked feature position displacements. To improve on this, a novel and robust biased linear predictor is proposed in this paper. Multiple linear predictors are grouped into a rigid flock to increase robustness. To further improve tracking accuracy, a novel probabilistic selection method is used to identify relevant visual areas for tracking a feature point. These selected flocks are then combined into a hierarchical multi-resolution LP model. Experiment...
Eng-Jon Ong, Yuxuan Lan, Barry Theobald, Richard H
Added 18 Feb 2011
Updated 18 Feb 2011
Type Journal
Year 2009
Where ICCV
Authors Eng-Jon Ong, Yuxuan Lan, Barry Theobald, Richard Harvey, Richard Bowden
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