Title :
Robust facial feature tracking using selected multi-resolution linear predictors
Author :
Ong, Eng-Jon ; Lan, Yuxuan ; Theobald, Barry ; Harvey, Richard ; Bowden, Richard
Author_Institution :
CVSSP, University of Surrey, UK
fDate :
Sept. 29 2009-Oct. 2 2009
Abstract :
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. Experimental results also show that this method performs more robustly and accurately than AAMs, without any a priori shape information and with minimal training examples.
Keywords :
Active appearance model; Active contours; Active shape model; Facial features; Lips; Markov random fields; Robustness; Tracking; Training data; Vectors;
Conference_Titel :
Computer Vision, 2009 IEEE 12th International Conference on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4244-4420-5
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2009.5459283