ArticleBehavior research methods2020
FaReT: A free and open-source toolkit of three-dimensional models and software to study face perception.
Article in Behavior research methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- Shape information used for face identity and expression recognition is highly versatile and context specific.Scientific reports · 2026Article
- Configural processing as an optimized strategy for robust object recognition in neural networks.Communications biology · 2025Article
- Face shape and motion are perceptually separable: Support for a revised model of face processing.Psychonomic bulletin & review · 2024Article
- Vienna Talking Faces (ViTaFa): A multimodal person database with synchronized videos, images, and voices.Behavior research methods · 2024Article
- High-Definition Transcranial Direct Current Stimulation in the Right Ventrolateral Prefrontal Cortex Lengthens Sustained Attention in Virtual Reality.Bioengineering (Basel, Switzerland) · 2023Article
- Testing Bayesian models of belief updating in the context of depressive symptomatology.International journal of methods in psychiatric research · 2023Article
- A computational account of the mechanisms underlying face perception biases in depression.Journal of abnormal psychology · 2021Article
- Adaptation aftereffects reveal how categorization training changes the encoding of face identity.Journal of vision · 2020Article
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3 authors.
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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.
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