The preprocessing portion of the FRGC will measure the impact of new preprocessing algorithms on recognition performance. These new algorithms work by preprocessing a facial image to correct for lighting and pose prior to being processed through a face recognition system. These advances have lead to the development of new computer algorithms that can automatically correct for lighting and pose changes in facial imagery. In the last couple years there have been advances in computer graphics and computer vision on modeling lighting and pose changes in facial imagery. Because the shape of faces is not affected by changes in lighting or pose, 3D face recognition has the potential to improve performance under these conditions. In current face recognition systems, changes in lighting (illumination) and pose of the face reduce performance. Three-dimensional (3D) face recognition algorithms identify faces from the 3D shape of a person's face. The FRGC will facilitate the development of new algorithms that take advantage of the additional information inherent in high resolution images. In the FRGC, high resolution images consist of facial images with 250 pixels between the centers of the eyes on average. In current images there are 40 to 60 pixels between the centers of the eyes (10,000 to 20,000 pixels on the face). The traditional method for measuring the size of a face is the number of pixels between the centers of the eyes. Current face recognition systems are designed to work on relatively small still facial images. The FRGC is simultaneously pursuing and will assess the merit of all three techniques. There are three main contenders for improving face recognition algorithms: high resolution images, three-dimensional (3D) face recognition, and new preprocessing techniques. The set of defined experiments assists researchers and developers in making progress on meeting the new performance goals. The FRGC challenge problems include sufficient data to overcome this impediment. One of the impediments to developing improved face recognition is the lack of data. Each challenge problem consisted of a data set of facial images and a defined set of experiments. The FRGC consisted of progressively difficult challenge problems.
The FRGC was open to face recognition researchers and developers in companies, academia, and research institutions. FRGC developed new face recognition techniques and prototype systems while increasing performance by an order of magnitude. The primary goal of the FRGC was to promote and advance face recognition technology designed to support existing face recognition efforts in the U.S. The Face Recognition Grand Challenge (FRGC) was conducted in an effort to fulfill the promise of these new techniques. These techniques hold the promise of reducing the error rate in face recognition systems by an order of magnitude over the Face Recognition Vendor Test (FRVT) 2002 results. This renewed interest has been fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Not since the mid 1990s has there been such a renewed interest in developing new methods for automatic face recognition.