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DEVELOPER GAINS

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The report shows accuracy improvements over time for this developer. The traces correspond to the datasets named in the legend. The FMR is fixed independently for each dataset to the value given in the y-axis label.

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COMPARATIVE ACCURACY

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For each dataset, the panels show false non-match rates vs. false match rates for clearviewai_000 and several of the most accurate algorithms listed in the caption. The most accurate algorithms vary by dataset. When negative values appear on the vertical axis, they are logarithms of FNMR. Use mouseover to see FNMR, FMR and threshold values.

Dataset comparison: false non-match rates vs. false match rates for each dataset. Use mouseover to see FNMR, FMR and threshold values.

To inform threshold setting, the two panels show, respectively, FMR and FNMR as a function of threshold. Many applications will use the threshold to target a specific FMR, established by policy. For a given threshold, FNMR variation is expected across datasets due, primarily, to quality and agageing differences. FMR differences may be due to different demographic composition - see figure below - or other factors.

For non-frontal visa-like photos compared with border crossing images, FNMR(T) and FMR(T) for various binned head yaw angles.

For mugshot images, FNMR as a function of elapsed time between initial enrollment and second verification images. The panels are for some more and less accurate algorithms, and the target of this report. The four traces correspond to images annotated with codes for black female, black male, white female, white male. The threshold is fixed for each algorithm to give FMR = 0.00001 over all approximately 10^8 impostor comparisons. For short time-lapses, the most accurate algorithms give very few errors (FNMR < 0.001) so that the bootstrap uncertainty estimates given by the blue ribbon are high. The dashed line gives the mean of the four demographic values.

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FACE MASK EFFECTS

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