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    Model for predicting perception of facial action unit activation using virtual humans

    McDonnell, Rachel and Zibrek, Katja and Carrigan, Emma and Dahyot, Rozenn (2021) Model for predicting perception of facial action unit activation using virtual humans. Computers & Graphics, 100. pp. 81-92. ISSN 0097-8493

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    Blendshape facial rigs are used extensively in the industry for facial animation of virtual humans. However, storing and manipulating large numbers of facial meshes (blendshapes) is costly in terms of memory and computation for gaming applications. Blendshape rigs are comprised of sets of semantically-meaningful expressions, which govern how expressive the character will be, often based on Action Units from the Facial Action Coding System (FACS). However, the relative perceptual importance of blendshapes has not yet been investigated. Research in Psychology and Neuroscience has shown that our brains process faces differently than other objects so we postulate that the perception of facial expressions will be feature-dependent rather than based purely on the amount of movement required to make the expression. Therefore, we believe that perception of blendshape visibility will not be reliably predicted by numerical calculations of the difference between the expression and the neutral mesh. In this paper, we explore the noticeability of blendshapes under different activation levels, and present new perceptually-based models to predict perceptual importance of blendshapes. The models predict visibility based on commonly-used geometry and image-based metrics.

    Item Type: Article
    Additional Information: Supplementary materials © 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( ) Cite as: Rachel McDonnell, Katja Zibrek, Emma Carrigan, Rozenn Dahyot, Model for predicting perception of facial action unit activation using virtual humans , Computers & Graphics, Volume 100, 2021, Pages 81-92, ISSN 0097-8493, (
    Keywords: Computers and graphics; Formatting; Guidelines;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15110
    Identification Number:
    Depositing User: Rozenn Dahyot
    Date Deposited: 09 Dec 2021 16:08
    Journal or Publication Title: Computers & Graphics
    Publisher: Elsevier
    Refereed: No
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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