The Florence 4D Facial Expression Dataset
F. Principi, S. Berretti, C. Ferrari, N. Otberdout, M. Daoudi, A. Del, Bimbo

TL;DR
The Florence 4D Facial Expression Dataset provides a large, diverse collection of dynamic 3D face sequences, enabling advanced research in facial expression analysis by overcoming previous data limitations.
Contribution
This paper introduces the first large-scale 4D facial expression dataset with synthetic and real identities, capturing a wide range of expressions and transitions, unavailable in existing datasets.
Findings
Dataset includes diverse identities and expressions.
Baseline experiments demonstrate dataset complexity.
Enables new applications in facial expression analysis.
Abstract
Human facial expressions change dynamically, so their recognition / analysis should be conducted by accounting for the temporal evolution of face deformations either in 2D or 3D. While abundant 2D video data do exist, this is not the case in 3D, where few 3D dynamic (4D) datasets were released for public use. The negative consequence of this scarcity of data is amplified by current deep learning based-methods for facial expression analysis that require large quantities of variegate samples to be effectively trained. With the aim of smoothing such limitations, in this paper we propose a large dataset, named Florence 4D, composed of dynamic sequences of 3D face models, where a combination of synthetic and real identities exhibit an unprecedented variety of 4D facial expressions, with variations that include the classical neutral-apex transition, but generalize to expression-to-expression.…
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Taxonomy
TopicsFace recognition and analysis · Face and Expression Recognition · Facial Nerve Paralysis Treatment and Research
MethodsFlorence
