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...

• Type: Journal
• Year: 2009
• Where: ICCV
• Authors: Eng-Jon Ong, Yuxuan Lan, Barry Theobald, Richard Harvey, Richard Bowden
• Keywords: Computer Vision, Facial Features, ICCV 2009, Learnt Data-driven Approach, Linear Predictors