research-article

Perception of linear and nonlinear motion properties using a FACS validated 3D facial model

Authors:

Adrian HiltonAuthors Info & Claims

APGV '10: Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization

Pages 101 - 108

https://doi.org/10.1145/1836248.1836268

Published: 23 July 2010 Publication History

Abstract

In this paper we present the first Facial Action Coding System (FACS) valid model to be based on dynamic 3D scans of human faces for use in graphics and psychological research. The model consists of FACS Action Unit (AU) based parameters and has been independently validated by FACS experts. Using this model, we explore the perceptual differences between linear facial motions -- represented by a linear blend shape approach -- and real facial motions that have been synthesized through the 3D facial model. Through numerical measures and visualizations, we show that this latter type of motion is geometrically nonlinear in terms of its vertices. In experiments, we explore the perceptual benefits of nonlinear motion for different AUs. Our results are insightful for designers of animation systems both in the entertainment industry and in scientific research. They reveal a significant overall benefit to using captured nonlinear geometric vertex motion over linear blend shape motion. However, our findings suggest that not all motions need to be animated nonlinearly. The advantage may depend on the type of facial action being produced and the phase of the movement.

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Cited By

Becker CConduit RChouinard PLaycock R(2024)Can deepfakes be used to study emotion perception? A comparison of dynamic face stimuliBehavior Research Methods10.3758/s13428-024-02443-y56:7(7674-7690)Online publication date: 4-Jun-2024
https://doi.org/10.3758/s13428-024-02443-y
Richoz AStacchi LSchaller PLao JPapinutto MTiccinelli VCaldara R(2024)Recognizing facial expressions of emotion amid noise: A dynamic advantageJournal of Vision10.1167/jov.24.1.724:1(7)Online publication date: 10-Jan-2024
https://doi.org/10.1167/jov.24.1.7
Evans TCarlson JZuberer AFry RAgnoli SBritton JDeGutis JEsterman M(2024)A preliminary characterization of the psychometric properties and generalizability of a novel social approach-avoidance paradigmMotivation and Emotion10.1007/s11031-024-10076-z48:3(278-294)Online publication date: 6-Jul-2024
https://doi.org/10.1007/s11031-024-10076-z
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Index Terms

Perception of linear and nonlinear motion properties using a FACS validated 3D facial model

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Published In

cover image ACM Conferences

APGV '10: Proceedings of the 7th Symposium on Applied Perception in Graphics and Visualization

July 2010

171 pages

ISBN:9781450302487

DOI:10.1145/1836248

Conference Chairs:
Diego Gutierrez
Universidad de Zaragoza
,
Joe Kearney
University of Iowa
,
Program Chairs:
Marty Banks
University of California, Berkeley
,
Katerina Mania
Technical University of Crete

Copyright © 2010 ACM.

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 [email protected]

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 July 2010

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SIGGRAPH

APGV '10: ACM Symposium on Applied Perception in Graphics & Visualization 2010

July 23 - 24, 2010

California, Los Angeles

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Overall Acceptance Rate 19 of 33 submissions, 58%

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Cited By

Becker CConduit RChouinard PLaycock R(2024)Can deepfakes be used to study emotion perception? A comparison of dynamic face stimuliBehavior Research Methods10.3758/s13428-024-02443-y56:7(7674-7690)Online publication date: 4-Jun-2024
https://doi.org/10.3758/s13428-024-02443-y
Richoz AStacchi LSchaller PLao JPapinutto MTiccinelli VCaldara R(2024)Recognizing facial expressions of emotion amid noise: A dynamic advantageJournal of Vision10.1167/jov.24.1.724:1(7)Online publication date: 10-Jan-2024
https://doi.org/10.1167/jov.24.1.7
Evans TCarlson JZuberer AFry RAgnoli SBritton JDeGutis JEsterman M(2024)A preliminary characterization of the psychometric properties and generalizability of a novel social approach-avoidance paradigmMotivation and Emotion10.1007/s11031-024-10076-z48:3(278-294)Online publication date: 6-Jul-2024
https://doi.org/10.1007/s11031-024-10076-z
Becker CConduit RChouinard PLaycock R(2024)A Dynamic Disadvantage? Social Perceptions of Dynamic Morphed Emotions Differ from Videos and PhotosJournal of Nonverbal Behavior10.1007/s10919-023-00448-348:2(303-322)Online publication date: 13-Jan-2024
https://doi.org/10.1007/s10919-023-00448-3
Marcoux ATessier MJackson PLugrin BLatoschik Mvon Mammen SKopp SPécune FPelachaud C(2023)Nonverbal Markers of Empathy in Virtual Healthcare ProfessionalsProceedings of the 23rd ACM International Conference on Intelligent Virtual Agents10.1145/3570945.3607291(1-4)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3570945.3607291
Snoek LJack RSchyns PGarrod OMittenbühler MChen COosterwijk SScholte H(2023)Testing, explaining, and exploring models of facial expressions of emotionsScience Advances10.1126/sciadv.abq84219:6Online publication date: 10-Feb-2023
https://doi.org/10.1126/sciadv.abq8421
Krumhuber ESkora LHill HLander K(2023)The role of facial movements in emotion recognitionNature Reviews Psychology10.1038/s44159-023-00172-12:5(283-296)Online publication date: 27-Mar-2023
https://doi.org/10.1038/s44159-023-00172-1
Gilbert MDemarchi SUrdapilleta I(2021)FACSHuman, a software program for creating experimental material by modeling 3D facial expressionsBehavior Research Methods10.3758/s13428-021-01559-953:5(2252-2272)Online publication date: 6-Apr-2021
https://doi.org/10.3758/s13428-021-01559-9
Ariano LFerrari CBerretti S(2021)Learning 3DMM Deformation Coefficients for Action Unit DetectionMachine Learning and Metaheuristics Algorithms, and Applications10.1007/978-981-16-0419-5_1(1-14)Online publication date: 6-Feb-2021
https://doi.org/10.1007/978-981-16-0419-5_1
Barabanschikov VKorolkova O(2020)Perception of “Live” Facial ExpressionsExperimental Psychology (Russia)Экспериментальная психология10.17759/exppsy.202013030513:3(55-73)Online publication date: 2020
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