Literature DB >> 32519291

FaReT: A free and open-source toolkit of three-dimensional models and software to study face perception.

Jason Hays1, Claudia Wong1, Fabian A Soto2.   

Abstract

A problem in the study of face perception is that results can be confounded by poor stimulus control. Ideally, experiments should precisely manipulate facial features under study and tightly control irrelevant features. Software for 3D face modeling provides such control, but there is a lack of free and open source alternatives specifically created for face perception research. Here, we provide such tools by expanding the open-source software MakeHuman. We present a database of 27 identity models and six expression pose models (sadness, anger, happiness, disgust, fear, and surprise), together with software to manipulate the models in ways that are common in the face perception literature, allowing researchers to: (1) create a sequence of renders from interpolations between two or more 3D models (differing in identity, expression, and/or pose), resulting in a "morphing" sequence; (2) create renders by extrapolation in a direction of face space, obtaining 3D "anti-faces" and caricatures; (3) obtain videos of dynamic faces from rendered images; (4) obtain average face models; (5) standardize a set of models so that they differ only in selected facial shape features, and (6) communicate with experiment software (e.g., PsychoPy) to render faces dynamically online. These tools vastly improve both the speed at which face stimuli can be produced and the level of control that researchers have over face stimuli. We validate the face model database and software tools through a small study on human perceptual judgments of stimuli produced with the toolkit.

Entities:  

Keywords:  Computer-generated faces; Dynamic faces; Face database; Face expression; Face identity; Face morphing

Mesh:

Year:  2020        PMID: 32519291      PMCID: PMC8893601          DOI: 10.3758/s13428-020-01421-4

Source DB:  PubMed          Journal:  Behav Res Methods        ISSN: 1554-351X


  44 in total

1.  Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces.

Authors:  Jakob H Macke; Felix A Wichmann
Journal:  J Vis       Date:  2010-05-01       Impact factor: 2.240

2.  The MR2: A multi-racial, mega-resolution database of facial stimuli.

Authors:  Nina Strohminger; Kurt Gray; Vladimir Chituc; Joseph Heffner; Chelsea Schein; Titus Brooks Heagins
Journal:  Behav Res Methods       Date:  2016-09

3.  Spatial cues influence the visual perception of gender.

Authors:  Sarah Ariel Lamer; Max Weisbuch; Timothy D Sweeny
Journal:  J Exp Psychol Gen       Date:  2017-07-17

4.  Symmetrical interaction of sex and expression in face classification tasks.

Authors:  Luis Aguado; Ana García-Gutierrez; Ignacio Serrano-Pedraza
Journal:  Atten Percept Psychophys       Date:  2009-01       Impact factor: 2.199

5.  Categorization training increases the perceptual separability of novel dimensions.

Authors:  Fabian A Soto; F Gregory Ashby
Journal:  Cognition       Date:  2015-03-25

6.  The Chicago face database: A free stimulus set of faces and norming data.

Authors:  Debbie S Ma; Joshua Correll; Bernd Wittenbrink
Journal:  Behav Res Methods       Date:  2015-12

7.  Novel representations that support rule-based categorization are acquired on-the-fly during category learning.

Authors:  Fabian A Soto; F Gregory Ashby
Journal:  Psychol Res       Date:  2019-02-26

8.  QUEST: a Bayesian adaptive psychometric method.

Authors:  A B Watson; D G Pelli
Journal:  Percept Psychophys       Date:  1983-02

9.  Angry facial expressions bias gender categorization in children and adults: behavioral and computational evidence.

Authors:  Laurie Bayet; Olivier Pascalis; Paul C Quinn; Kang Lee; Édouard Gentaz; James W Tanaka
Journal:  Front Psychol       Date:  2015-03-26

Review 10.  The neural mechanisms for the recognition of face identity in humans.

Authors:  Stefano Anzellotti; Alfonso Caramazza
Journal:  Front Psychol       Date:  2014-06-26
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  2 in total

1.  A computational account of the mechanisms underlying face perception biases in depression.

Authors:  Fabian A Soto; Rochelle A Stewart; Sanaz Hosseini; Jason Hays; Christopher G Beevers
Journal:  J Abnorm Psychol       Date:  2021-07

2.  Adaptation aftereffects reveal how categorization training changes the encoding of face identity.

Authors:  Fabian A Soto; Karla Escobar; Jefferson Salan
Journal:  J Vis       Date:  2020-10-01       Impact factor: 2.240

  2 in total

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