Developer Name: Incode Technologies Inc | Algorithm Name: incode_011 | Algorithm Type: 1:1 Verification

Date of Algorithm Submission: 2022_08_10 | Date of Report Card Generation: 2024-03-27

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.

For each dataset, the panels show false non-match rates vs. false match rates for incode_011 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.

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 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.

The figure shows similarity scores for 12 genuine and 8 impostor image pairs used in the May 2018 paper https://doi.org/10.1073/pnas.1721355115 Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms (Phillips et al.). The threshold (red horizontal line) is a value calibrated to give FMR = 0.0001 on mugshot images. Points above the threshold correspond to pairs determined to be genuine, and points below the threshold correspond to pairs determined to be impostors. If the determined class (genuine or impostor) matches the real class, points will be blue; if not, red. An X represents face detection failure in either of the images in the pair. Note that the sample size (n=20) is small, and the figure may change substantially if larger or different sets are used. The images can be viewed at https://www.pnas.org/doi/suppl/10.1073/pnas.1721355115/suppl_file/pnas.1721355115.sapp.pdf, where Gen 01 corresponds to Same-Identity Pair 1, Gen 02 corresponds to Same-Identity Pair 2, and so on.

For women, left, and men, the panels show false non-match rates when mediocre border cross photos are compared against high quality reference application portraits collected from individuals born in the country identified on the horizontal axis and aged either above or below 45 years of age at the time of the application photo. The square dots give the empirical FNMR point estimate. The vertical lines give bootstrap 95-percent confidence intervals around the point estimate. The intervals are wider when the country and age group is less-represented in this dataset. Overlapping intervals is an indication of no significant difference. Low FNMR values are synonymous with high accuracy.