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Online learning of robust facial feature trackers

Sheerman-Chase, T, Ong, E-J and Bowden, R (2009) Online learning of robust facial feature trackers In: ICCV 2009, 2009-09-27 - 2009-10-04, Kyoto, Japan.

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Abstract

This paper presents a head pose and facial feature estimation technique that works over a wide range of pose variations without a priori knowledge of the appearance of the face. Using simple LK trackers, head pose is estimated by Levenberg-Marquardt (LM) pose estimation using the feature tracking as constraints. Factored sampling and RANSAC are employed to both provide a robust pose estimate and identify tracker drift by constraining outliers in the estimation process. The system provides both a head pose estimate and the position of facial features and is capable of tracking over a wide range of head poses.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Copyright 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Depositing User: Symplectic Elements
Date Deposited: 22 May 2012 14:18
Last Modified: 23 Sep 2013 19:24
URI: http://epubs.surrey.ac.uk/id/eprint/531470

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