UIBVFED: Virtual facial expression dataset.
Miquel Mascaró Oliver1, Esperança Amengual Alcover1
1Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain.
Plos One
|April 7, 2020
Summary
We created a novel facial expression dataset with 640 images from 20 virtual characters, aiding artists and affective computing researchers. This dataset supports diverse real-world facial expression classification tasks.
Area of Science:
- Affective Computing
- Computer Vision
- Digital Art
Background:
- Facial expression classification demands extensive datasets for real-world diversity.
- Existing public databases often lack focus on artistic creation needs.
Purpose of the Study:
- To develop an innovative facial expression dataset beneficial for both artists and affective computing researchers.
- To provide an interactively manageable dataset via user-friendly software.
Main Methods:
- Generated a dataset of 640 facial images from 20 distinct virtual characters.
- Each character produced 32 unique facial expressions.
- Classified expressions based on Gary Faigin's six universal expressions.
Main Results:
- The dataset comprises diverse virtual characters (10 male, 10 female, aged 20-80, various ethnicities).
- Includes 32 distinct facial expressions per character, totaling 640 images.
- Facial expressions are categorized using the six universal expressions framework.
Conclusions:
- The developed dataset bridges the gap for artistic creation and affective computing research.
- Offers a versatile resource for training and evaluating facial expression recognition systems.
- The accompanying software facilitates interactive dataset management and usability.
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