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A Unified Tensor-based Active Appearance Model

Published:15 October 2019Publication History
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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.

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      cover image ACM Transactions on Multimedia Computing, Communications, and Applications
      ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 15, Issue 3s
      Special Issue on Face Analysis for Applications and Special Issue on Affective Computing for Large-Scale Heterogeneous Multimedia Data
      November 2019
      304 pages
      ISSN:1551-6857
      EISSN:1551-6865
      DOI:10.1145/3368027
      Issue’s Table of Contents

      Copyright © 2019 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 15 October 2019
      • Accepted: 1 May 2019
      • Revised: 1 February 2019
      • Received: 1 November 2018
      Published in tomm Volume 15, Issue 3s

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