FRGC (Face Recognition Grand Challenge)

The data for FRGC consists of 50,000 recordings divided into training and validation partitions. The training partition is designed for training algorithms and the validation partition is for assessing performance of an approach in a laboratory setting. The validation partition consists of data from 4,003 subject sessions. A subject session is the set of all images of a person taken each time a person's biometric data is collected and consists of four controlled still images, two uncontrolled still images, and one three-dimensional image. The controlled images were taken in a studio setting, are full frontal facial images taken under two lighting conditions and with two facial expressions (smiling and neutral). The uncontrolled images were taken in varying illumination conditions; e.g., hallways, atriums, or outside. Each set of uncontrolled images contains two expressions, smiling and neutral. The 3D image was taken under controlled illumination conditions. The 3D images consist of both a range and a texture image. The 3D images were acquired by a Minolta Vivid 900/910 series sensor.

Source: https://www.nist.gov/programs-projects/face-recognition-grand-challenge-frgc Image Source: https://www.researchgate.net/figure/Example-of-images-in-FRGC-20-dataset-The-dataset-consist-of-controlled-images-a-c-as_fig10_285759105