Date: November 2023.
Source: Pattern Recognition Letters, Volume 175, Pages 23-29, ISSN 0167-8655, https://doi.org/10.1016/j.patrec.2023.09.015.
Highlights:
• A new 3D face dataset including synchronized pairs of high and low resolution meshes.
• Asymmetric and natural facial expressions for face rehabilitation exercises.
• Experimental results highlighting the open challenge of cross-resolution 3D face recognition and reconstruction.
Abstract: In the literature, several 3D face datasets have been collected, aiming at advancing the field of 3D face analysis from different perspectives. Data collection generally follows specific research needs, and the existing 3D face datasets all have different characteristics that are tailored for investigating different tasks, encompassing face recognition, facial expressions and emotions analysis, 3D face reconstruction. However, the majority of these datasets are either collected with high-resolution scanners, or consumer level devices, such as the Kinect, the latter being motivated by the burdensome and costly process of collecting high-quality scans. Differently from 2D imagery, the difference in resolution in 3D data represents a non negligible problem that is under-investigated, and still prevents the successful development of methods that can work in real scenarios. In this paper, we propose a new 3D face dataset, named “Florence Multi-Resolution 3D Facial Expression” (Florence 3DMRE), which aims at bridging the gap between high- and low-resolution 3D face datasets. Its peculiarity consists in (1) including high-resolution (HR) models obtained with a HR scanner, and paired samples collected with a Kinect sensor, (2) LR and HR scans are synchronized and capture extreme and asymmetric facial deformations as used in facial rehabilitation exercises. In total, our dataset consists of 14 subjects, each performing 19 complex and asymmetric expressions. For each of them, we collected a high-resolution scan, and an RGB-D sequence. Finally, to highlight the value of the dataset and the challenges it introduces, we use the collected data to perform baseline experiments for cross-resolution 3D face recognition and reconstruction. The dataset is released for research purposes only, and complies to GDPR for data treatment.

Article: The Florence multi-resolution 3D facial expression dataset.
Authors: Claudio Ferrari, Stefano Berretti, Pietro Pala, Alberto Del Bimbo.

Related Articles:

  1. A multi-ancestry GWAS meta-analysis of facial features and its application in predicting archaic human features. S Du, J Chen, J Li, W Qian, S Wu, Q Peng, Y Liu, T Pan, Y Li, SS Hadi, J Tan, Z Yuan, J Wang, K Tang, Z Wang, Y Wen, X Dong, W Zhou, A Ruiz-Linares, Y Shi, L Jin, F Liu, M Zhang, S Wang.
  2. MimicME: A Large Scale Diverse 4D Database for Facial Expression Analysis. A Papaioannou et al.
  3. The Menpo Benchmark for Multi-pose 2D and 3D Facial Landmark Localisation and Tracking. J Deng, A Roussos, G Chrysos et al.
  4. Landmark movement trajectory: A simple reference to understand the dominant muscle contraction in a dynamic smile expression. W Sun, M Xu, T Song.