The Florence 4D Facial Expression Dataset
Dataset of 4D dynamic sequences of 3D faces with varying facial expressions
In this work, 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. All these characteristics are not exposed by any of the existing 4D datasets, and they cannot even be obtained by combining more than one dataset. We strongly believe that making such a data corpus publicly available to the community will allow designing and experimenting new applications that were not possible to investigate till now. To show to some extent the difficulty of our data in terms of different identities and varying expressions, we also report a baseline experimentation on the proposed dataset that can be used as a baseline.
We identified a key missing aspect in the current literature of 4D face analysis, that is the ability of modeling complex, non-standard expressions, and transitions between them. Indeed, current models and datasets are limited to the case, where a facial expression is performed assuming a neutral-apex-neutral transition. This does not hold in the real world, where people continuously switch between one facial expression to another. These observations motivated us to generate the proposed Florence 4D dataset.
Florence 4D includes real and synthetic identities from different sources: (a) CoMA identities; (b) synthetic identities; (c) high-resolution 3D face scans of real identities.
- CoMA identities: The CoMA dataset is largely used for the analysis of dynamic facial expressions. An important characteristic of this dataset that contributed to its large use is the fixed topology, according to which all the scans have 5,023 vertices that are connected in a fixed way to form meshes with 9,976 triangular facets. The dataset includes 12 real identities (5 females and 7 males).
- Synthetic identities: On the Web, many 3D models of synthetic facial characters, either females or males, can be purchased or downloaded for free. Using these online resources, we were able to add 63 synthetic identities (33 females and 30 males) to the data, selecting those that allow editing and redistribution for non-commercial purposes. Subjects are split into three ethnic groups, Afro (16%), Asian (13%), and Caucasian (71%). Because such identities are synthetic, the resulting meshes are defect free, and perfectly symmetric, which is different from real faces. To make models more realistic, morphing solutions were applied to include face asymmetries.
- 3D real scans: We acquired 3D scans of 20 subjects (5 females and 15 males) with a 3DmD HR scanner. Subjects are mainly students and university personnel, 30 years old on average. Meshes have approximately 30k vertices. Written consents were collected for these subjects for using their 3D face scans.