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Non-linear predictors for facial feature tracking across pose and expression

Sheerman-Chase, T, Ong, E-J and Bowden, R (2013) Non-linear predictors for facial feature tracking across pose and expression 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2013.

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Abstract

This paper proposes a non-linear predictor for estimating the displacement of tracked feature points on faces that exhibit significant variations across pose and expression. Existing methods such as linear predictors, ASMs or AAMs are limited to a narrow range in pose. In order to track across a large pose range, separate pose-specific models are required that are then coupled via a pose-estimator. In our approach, we neither require a set of pose-specific models nor a pose-estimator. Using just a single tracking model, we are able to robustly and accurately track across a wide range of expression on poses. This is achieved by gradient boosting of regression trees for predicting the displacement vectors of tracked points. Additionally, we propose a novel algorithm for simultaneously configuring this hierarchical set of trackers for optimal tracking results. Experiments were carried out on sequences of naturalistic conversation and sequences with large pose and expression changes. The results show that the proposed method is superior to state of the art methods, in being able to robustly track a set of facial points whilst gracefully recovering from tracking failures. © 2013 IEEE.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Sheerman-Chase, TUNSPECIFIEDUNSPECIFIED
Ong, E-JUNSPECIFIEDUNSPECIFIED
Bowden, RUNSPECIFIEDUNSPECIFIED
Date : 22 April 2013
Identification Number : 10.1109/FG.2013.6553763
Additional Information : © 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 17 Nov 2015 18:11
Last Modified : 17 Nov 2015 18:11
URI: http://epubs.surrey.ac.uk/id/eprint/808963

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