Datasets

Multi-view Facial Image Dataset Based on LFW: Using software that is based on the code that accompanies  this paper  a set of synthetically generated multi-view facial images has been created within OpenDR H2020 research project by Aristotle University of Thessaloniki based on the  LFW   image dataset which is a facial image dataset that consists of 13,233 facial images in the wild for 5,749 person identities collected from the Web. The resulting set, named AUTH-OpenDR Augmented LFW (AUTH-OpenDR ALFW), consists of 5,749 person identities. From each image of these subjects (13,233 in total), 13 synthetic images generated by yaw axis camera rotation in the interval [0◦: +60◦ ] with step +5◦ are obtained. Moreover, 10 synthetic images generated by pitch axis camera rotation in the interval [0◦ : +45◦ ] with step +5◦ are also created for each facial image of the aforementioned dataset. The dataset structure is as follows. Two folders exist for every person identity with his/her name as folder name: one of these folders contains the aligned and the other the original facial images (this is also indicated in the folder name). The file names of the images in these folders are as follows: {Name of the person} {current ID in case of multiplicity} {yaw/pitch direction} {angle in rad}.jpg. The ID is used to distinguish between the various images of the same person, if more than one are available. Examples: ”Alicia Witt 0001 pitch 0.26.jpg” or ”Alicia Witt 0002 yaw 0.52.jpg”. The ALFW dataset is covered by a Creative Commons Attribution-NonCommercial 4.0 International license and can be downloaded from this FTP site.

                                                                                    Table 1: Summary of ALFW Multi-view Facial Image Dataset

                                                                                                          Samples from the ALFW dataset

Multi-view Facial Image Dataset Based on CelebA: For performance evaluation or training of face recognition methods, a dataset of facial images from several viewing angles was created by Aristotle University of Thessaloniki based on the CelebA image dataset, using the software that was created in OpenDR H2020 research project based on this paper and the respective code provided by the authors. CelebA is a largescale facial dataset and consists of 202,599 facial images of 10,177 celebrities captured in the wild. The new dataset is named AUTH-OpenDR Augmented CelebA (AUTH-OpenDR ACelebA). The set was generated from 140,000 facial images corresponding to 9161 persons, i.e. a subset of CelebA was used. For each CelebA image used, 13 synthetic images generated by yaw axis camera rotation in the interval [0◦ : +60◦ ] with step +5◦ were obtained. Moreover, 10 synthetic images generated by pitch axis camera rotation in the interval [0◦: +45◦] with step +5◦ are also created for each facial image of the aforementioned dataset. Since CelebA license does not allow distribution of derivative work we do not make AcelebA directly available but instead provide instructions and scripts on how to recreate it. The process that the user shall follow in order to reproduce the Augmented CelebA (ACelebA) is the following:

  • Download to your local folder the public available code of the Github repo Rotate-and-Render
  • Download the CelebA facial image dataset in a local folder
  • Download the python script Do_main_Person_Identity.py and identity_CelebA.csv from this FTP site
  • Create the folder: 3ddfa/Pre-processing
  • Add script and csv to this folder
  • Include the appropriate input paths in the script (rootdir-folder of CelebA dataset)Execute the command python3 Do_main_Person_Identity_ACelebA.py
  • Execute bash experiments/v100_test.sh with appropriate parameters (e.g. gpu_ids) and after modifying the last two lines as follows:

       --yaw_poses 0 5 10 15 20 25 30 35 40 45 50 55 60 \

       --pitch_poses 5 10 15 20 25 30 35 40 45 \

 

                                                                                 Table 2: Summary of and ACelebA Multi-view Facial Image Dataset