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A Unified Tensor-based Active Appearance Model

Feng, Zhen-Hua, Kittler, Josef, Christmas, Bill and Wu, Xiao-Jun (2019) A Unified Tensor-based Active Appearance Model ACM Transactions on Multimedia Computing, Communications and Applications.

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Abstract

Appearance variations result in many difficulties in face image analysis. To deal with this challenge, we present a Unified Tensor-based Active Appearance Model (UT-AAM) for jointly modelling the geometry and texture information of 2D faces. For each type of face information, namely shape and texture, we construct a unified tensor model capturing all relevant appearance variations. This contrasts with the variation-specific models of the classical tensor AAM. To achieve the unification across pose variations, a strategy for dealing with self-occluded faces is proposed to obtain consistent shape and texture representations of pose-varied faces. In addition, our UT-AAM is capable of constructing the model from an incomplete training dataset, using tensor completion methods. Last, we use an effective cascaded-regression-based method for UT-AAM fitting. With these advancements, the utility of UT-AAM in practice is considerably enhanced. As an example, we demonstrate the improvements in training facial landmark detectors through the use of UT-AAM to synthesise a large number of virtual samples. Experimental results obtained on a number of well-known face datasets demonstrate the merits of the proposed approach.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Feng, Zhen-Huaz.feng@surrey.ac.uk
Kittler, JosefJ.Kittler@surrey.ac.uk
Christmas, BillW.Christmas@surrey.ac.uk
Wu, Xiao-Jun
Date : 2019
Funders : Engineering and Physical Sciences Research Council (EPSRC)
Grant Title : FACER2VM
Copyright Disclaimer : © 2019 Association for Computing Machinery. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
Uncontrolled Keywords : Computing methodologies; Computer vision tasks; Face image analysis; Active appearance model; Tensor algebra; Missing training samples; Cascaded regression
Related URLs :
Depositing User : Clive Harris
Date Deposited : 17 Jun 2019 09:36
Last Modified : 17 Jun 2019 09:36
URI: http://epubs.surrey.ac.uk/id/eprint/852005

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