Dataset Information

UIBVFED: Virtual facial expression dataset.

ABSTRACT: Facial expression classification requires large amounts of data to reflect the diversity of conditions in the real world. Public databases support research tasks providing researchers an appropriate work framework. However, often these databases do not focus on artistic creation. We developed an innovative facial expression dataset that can help both artists and researchers in the field of affective computing. This dataset can be managed interactively by an intuitive and easy to use software application. The dataset is composed of 640 facial images from 20 virtual characters each creating 32 facial expressions. The avatars represent 10 men and 10 women, aged between 20 and 80, from different ethnicities. Expressions are classified by the six universal expressions according to Gary Faigin classification.

SUBMITTER: Oliver MM

PROVIDER: S-EPMC7135287 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

UIBVFED: Virtual facial expression dataset.

Oliver Miquel Mascaró MM Amengual Alcover Esperança E

PloS one 20200406 4

Facial expression classification requires large amounts of data to reflect the diversity of conditions in the real world. Public databases support research tasks providing researchers an appropriate work framework. However, often these databases do not focus on artistic creation. We developed an innovative facial expression dataset that can help both artists and researchers in the field of affective computing. This dataset can be managed interactively by an intuitive and easy to use software app ...[more]

PMID: 32251435

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Project description:Facial appearance transfer from donor to recipient in face transplantation is a concern. Previous studies of facial appearance transfer and facial appearance persistence (preservation of the recipient's facial likeness) in face transplants simulated using two-dimensional photographic manipulations found low facial appearance transfer (2.6 percent) and high facial appearance persistence (66 percent). Three-dimensional computer simulation of complex facial transplant patterns may improve the accuracy of facial appearance transfer and facial appearance persistence estimations.Three-dimensional virtual models of human faces were generated from deidentified computed tomographic angiographs and used as "donors" or "recipients" for virtual face transplantation. Surgical planning software was used to perform 73 virtual face transplantations by creating specific facial defects (mandibular, midface, or large) in the recipient models and restoring them with allografts extracted from the donor models. Twenty independent reviewers evaluated the resemblance of each resulting posttransplant model to the donor (facial appearance transfer) and recipient (facial appearance persistence). The results were analyzed using tests for equal results with one-sample and pairwise Rao-Scott Pearson chi-square testing, correcting for clustering and multiple testing.Overall rates of facial appearance persistence and facial appearance transfer were high (69.2 percent) and low (32.4 percent), respectively. The mandibular pattern had the highest rates of facial appearance persistence and lowest rates of facial appearance transfer. Facial appearance persistence and transfer were similar across sexes.Facial appearance persistence is high and facial appearance transfer is low after virtual face transplantation. Appearance transfer and persistence after virtual face transplantation are more dependent on the anatomy than on the size of transplanted facial aesthetic units. This information may reassure recipients of partial face transplants and donor families.

Dataset Information

UIBVFED: Virtual facial expression dataset.

Publications

UIBVFED: Virtual facial expression dataset.

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