Frontiers of Engineering Reports on Leading-Edge Engineering from the 2005 Symposium (2006) / Chapter Skim
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Designing Biometric Evaluations and Challenge Problems for Face-Recognition Systems
Designing Biometric Evaluations and Challenge Problems for Face-Recognition Systems
Pages 15-22
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From page 15... ...
From selfreported results on propriety data sets, it was not possible to make objective comparisons of different approaches or to access the best techniques. The tradition of challenge problems and evaluations in face recognition began with the FERET Program, which ran from 1993 to 1997.
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From page 16... ...
. The last FERET evaluation, in September 1996, measured performance on a data set of 1,196 people and 3,323 images.
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In FRVT 2000, the results obtained using two indoor data sets with different lighting were approximately the same. In both experiments, the best performer had a 90 percent verification rate and a FAR of 1 percent.
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Using FRVT 2002 data sets, we found that recognition levels using video sequences was the same as with still images. In summary, several key lessons were learned from FRVT 2002: · Given reasonable, controlled indoor lighting, the current state of the art in face recognition is 90 percent verification at a 1 percent FAR.
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From page 19... ...
at the same fixed FAR of 0.1 percent. SUMMARY OF GRAND CHALLENGE PERFORMANCE Participants in FRGC submitted raw similarity scores to FRGC organizers on January 14, 2005 (for a detailed description of the FRGC challenge problem, data, and experiments, see Phillips et al., 2005)
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From page 20... ...
In biometric evaluations, the set of images known to a system is called the target set, and the set of unknown images presented to a system is called the query set. In Experiment 1, the biometric samples in the target and query sets consist of a single, controlled still image.
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From page 21... ...
The results for Experiment 3 were obtained only three months after the first release of a large 3-D data set. By comparison, the results for still images are based on more than a decade of intensive research after the release of the first large still-image data sets.
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From page 22... ...
FRVT 2002, which supplied reported performance rates on a large data set of operational images, served as a baseline for progress under FRGC. FRGC is facilitating the development of the next generation of face recognition.
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