While not part of the FRVT series, the Face-in-Video-Evaluation (FIVE) conducted 2015-2016 will be of interest to the FRVT audience. the FIVE activity assessed face recognition capability in video sequences.
FRVT 2013 will test state-of-the-art face recognition performance. It will use very large sets of facial imagery to measure the accuracy and computational efficiency of face recognition algorithms developed in commercial and academic communities worldwide. The test itself will commence in late July 2012 and run through to the end of 2013. The detailed plans, procedures and progress of the test are documented on the FRVT 2013 homepage.
A program called FRVT 2010 was never conducted. Instead an equivalent program, the still image track of MBE 2010, was conducted with almost exactly the same goals as an FRVT test.
Moreover MBE 2010 instituted the methodologies used in FRVT 2013.
The FRVT 2006 measured performance with sequestered data (data not previously seen by the researchers or developers). A standard dataset and test methodology was employed so that all participants were evenly evaluated. The government provided both the test data and the test environment to participants. The test environment was called the Biometric Experimentation Environment (BEE). The BEE was the FRVT 2006 infrastructure. It allowed the experimenter to focus on the experiment by simplifying test data management, experiment configuration, and the processing of results.
FRVT 2002 consisted of two tests: the High Computational Intensity (HCInt) Test and the Medium Computational Intensity (MCInt) Test. Both test required the systems to be full automatic, and manual intervention was not allowed.
FRVT 2000 consisted of two components: the Recognition Performance Test and the Product Usability Test. The Recognition Performance Test was a technology evaluation. The goal of the Recognition Performance Test was to compare competing techniques for performing facial recognition. All systems were tested on a standardized database. The standard database ensured all systems were evaluated using the same images, which allowed for comparison of the core face recognition technology. The product usability test examined system properties for performing access control.
The goal of the FERET program was to develop automatic face recognition capabilities that could be employed to assist security, intelligence, and law enforcement personnel in the performance of their duties. The task of the sponsored research was to develop face recognition algorithms. The FERET database was collected to support the sponsored research and the FERET evaluations. The FERET evaluations were performed to measure progress in algorithm development and identify future research directions.