Florence Superface

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The Florence Superface dataset comprises low-resolution and high-resolution 3D scans aiming to investigate innovative 3D face recognition solutions that use scans at different resolutions. Currently, 20 subjects are included in the dataset, but enrolling is still ongoing. For each subject, the dataset includes:

  • A 2D/3D video sequence acquired with the Microsoft Kinect. During capture, subjects sit in front of the camera with the face at an approximate distance of 80cm from the sensor. Subjects are also asked to slightly rotate the head around the yaw axis up to an angle of about 60-70 degrees, so that both the left and right side of the face are visible to the sensor. This results in video sequences lasting approximately 10 to 15 sec. Videos are released as a sequence of depth (16 bits) and rgb (24 bits) frames in PNG format;
  • A 3D high-resolution face scan acquired with the 3dMD scanner: 3D mesh with about 40,000 vertices and 80,000 facets; texture stereo image with a resolution of 3341 x 2027 pixels. The geometry of the mesh is highly accurate with an average RMS error of about 0.2mm or better (VRML format).

Note: the dataset can be freely downloaded and used for research (no-profit) purposes. Publications that use this dataset must reference the following work: S. Berretti, A. Del Bimbo, P. Pala. “Superfaces: A Super-resolution Model for 3D Faces”, Fifth Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA’12), in conjunction with ECCV 2012, Firenze, 7 ottobre 2012.

Interested researchers can refer to the EURECOM Kinect Face Dataset at http://rgb-d.eurecom.fr, in order to test their algorithms on a separate dataset, and thus use diverse sets for training and test.

Please, if you use the dataset cite our papers as follows:

  author    = {Stefano Berretti and Alberto Del Bimbo and Pietro Pala},
  title     = {Superfaces: A Super-Resolution Model for 3D Faces},
  booktitle = {ECCV Workshops (1)},
  year      = {2012},
  pages     = {73-82},
  ee        = {http://dx.doi.org/10.1007/978-3-642-33863-2_8},
  crossref  = {DBLP:conf/eccv/2012w1},
  bibsource = {DBLP, http://dblp.uni-trier.de}

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