| API & CONOPS Document | Participation Agreement | Submission Form | [email protected] |
| Last Updated: February 27, 2026 |
The IREX 10: Identification Track assesses iris recognition performance for identification (a.k.a one-to-many) applications. Most flagship deployments of iris recognition operate in identification mode, providing services ranging from prison management, border security, expedited processing, and distribution of resources. Administered at the Image Group’s Biometrics Research Lab (BRL), developers submit their iris recognition software for testing over datasets sequestered at NIST. As an ongoing evaluation, developers may submit at any time.
The table below shows performance statistics for IREX 10 submissions. Results are shown only for the ‘most accurate’ submission from each developer, which is the one that produces the lowest FNIR @ FPIR = 0.01. Timing durations in black (submitted before January 1st, 2025) were computed on an Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz machine, and those in blue (submitted after January 1st, 2025) were computed on an Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz machine.
NOTE 2025-12-18: The duration values for algorithms submitted since 2025-09-04 are provisional. From that date, NIST ran all algorithms on a more recent Linux operating system (OS). The durations may be revised after the conclusion of our investigation into why numeric software is slower on identical hardware equipped with the new OS.
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence). |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
The number after the ± indicates either the 90% confidence interval (for accuracy) or the standard deviation (for times and sizes).
The table below shows performance statistics for all submissions to IREX 10. Many developers submitted multiple times. Timing durations in black (submitted before January 1st, 2025) were computed on an Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz machine, and those in blue (submitted after January 1st, 2025) were computed on an Intel(R) Xeon(R) Gold 6140 CPU @ 2.30GHz machine.
NOTE 2025-12-18: The duration values for algorithms submitted since 2025-09-04 are provisional. From that date, NIST ran all algorithms on a more recent Linux operating system (OS). The durations may be revised after the conclusion of our investigation into why numeric software is slower on identical hardware equipped with the new OS.
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence). |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Both eyes |
| Enrolled Population: | 500K people |
| Enrollment Method: | Both (left and right) iris images per enrollment template |
The number after the ± indicates either the 90% confidence interval (for accuracy) or the standard deviation (for times and sizes).
The table below shows performance statistics for IREX 10 submissions. Results are shown only for the ‘most accurate’ submission from each developer, which is the one that produces the lowest FNIR @ FPIR = 0.01.
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence). |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Single eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
The number after the ± indicates either the 90% confidence interval (for accuracy) or the standard deviation (for times and sizes).
The table below shows performance statistics for all submissions to IREX 10. Many developers submitted multiple times.
| Accuracy Metric : |
FNIR (i.e., “miss rate”) at an FPIR of 0.01 (± 90% confidence). |
| Dataset: | Operational Dataset 4th pull (stats on OPS4 images) |
| Samples used: | Single eye |
| Enrolled Population: | 1M irides (500K people) |
| Enrollment Method: | One iris image per enrollment template |
The number after the ± indicates either the 90% confidence interval (for accuracy) or the standard deviation (for times and sizes).
Core accuracy for the identification task can be characterized by Detection-error trade-off (DET) plots. Generally, curves lower down in a DET plot correspond to more accurate matchers.
The plots are interactive through the use of the Plotly.js graphing library. Mousing over the DET plots will display a menu at the top right which provides the capability to manipulate the plot in various ways and to download the plot as a png or the underlying data as a csv. Double-clicking on an item in the legend will display only that item in the plot; single-clicking will add/remove the item from the plot.