Facing Asymmetry Dataset

Dataset Release
We are pleased to announce the release of our "Facing Asymmetry Dataset," a comprehensive collection of simulated asymmetric faces relevant to understanding how neural networks react toward facial asymmetry during the six base emotions.
This dataset has been developed through causal interventions and contains 200 individuals. The data has been carefully curated and processed to ensure quality and consistency. Each person has optimized facial expression for 17 independent FER classifiers. Additionally, we provide the logit activations of the classifiers.
All resemblance to existing people is not intended and could only result from the underlying FLAME geometry model and the texture from the BaselFaceModel.
This dataset accompanies the upcoming ACCV 2024 publication: Facing Asymmetry - Uncovering the Causal Link between Facial Symmetry and Expressio Classifiers using Synthetic Interventions.
Dataset Details
Name: Facing AsymmetryDescription: Simulated facial asymmetry during the six base emotionsNumber of Examples: 200 individuals, with 17 expression classifiers, with each six emotionsData Type: images, CSV tables with logit activations, expression vectorsField: Computer Vision, Facial Expression Recognition, Facial PalsyUse and Citation
This dataset is intended for use in research. We encourage researchers and developers to utilize this resource and contribute to its further development.
To cite this dataset, please refer to the following paper:
Facing Asymmetry - Uncovering the Causal Link between Facial Symmetry and Expressio Classifiers using Synthetic Interventions
License and Permissions
This dataset is released under the CC BY 4.0 license. By downloading or using this dataset, you agree to the terms of this license.
Contact Information
If you have any questions or comments regarding this dataset, please do not hesitate to contact us at [e-mail]