Face Recognition Grand Challenge

[1] The FRGC v2 database created in 2005 has had a significant impact on the development of 3D face recognition.

The FRGC developed new face recognition techniques and prototype systems that significantly improved performance.

The FRGC consisted of progressively difficult challenge problems, each of which included a dataset of facial images and a defined set of experiments.

The challenge problems were designed to overcome one of the impediments to developing improved face recognition, which is the lack of data.

Current face recognition systems are designed to work with relatively small, static facial images.

The FRGC aims to foster the development of new algorithms that leverage the additional information present in high-resolution images.

These advances have led to the development of new algorithms that can automatically correct for lighting and pose changes before processing through a face recognition system.

Unlike previous face recognition datasets that focused on still images, the FRGC encompasses three modes: The third new aspect is the infrastructure.

This marks the first time a computational-experimental environment has supported a challenge problem in face recognition or biometrics.

With all three components, it is possible to run experiments 1 through 4, from processing raw images to producing Receiver Operating Characteristics (ROCs).

In experiment 2, each biometric sample consists of the four controlled images of a person taken in a subject session.

This article incorporates public domain material from NIST Face Recognition Grand Challenge.