# Windows Hello biometric requirements

Learn about the hardware requirements for biometric equipment, such as IR camera and fingerprint readers in order to support Windows Hello.

## Terminology

Term | Definition |
---|---|

False Accept Rate (FAR) | Represents the number of instances that a biometric identification solution verifies an unauthorized person. This is normally represented as a ratio of number of instances in a given population size, for example 1 in 100,000. This can also be represented as a percentage of occurrence, for example, 0.001 percent. This measurement is heavily considered the most important with regards to the security of the biometric algorithm. |

True Accept Rate (TAR) | Represents the number of instances a biometric identification solution verifies the authorized user correctly. This is normally represented as a percentage. It is always held that the sum of the True Accept Rate and False Reject Rate is 1. |

False Reject Rate (FRR) | Represents the number of instances a biometric identification solution fails to verify an authorized user correctly. Usually represented as a percentage, the sum of the True Accept Rate and False Reject Rate is 1. |

Confidence | The confidence in a claimed FAR represents the robustness of the analysis performed in verifying a claimed FAR. Depending on the target or claimed FAR and the importance of the target use case, confidence levels can be varied. |

Biometric sample | This refers to the complete biometric sample required to perform a verification operation. For example, a single fingerprint is required to perform a verification. |

Biometric spoof | This refers to a synthetic replica of a biometric sample. |

## Fingerprint reader requirements

Large Area sensors (a sensor matrix of 160 x160 Pixels or more at a dpi of 320 or greater):

- FAR < 0.001%.
- Effective, real world FRR with antispoofing or liveness detection <10%.
- presentation attack defense measures are a requirement.

Small Area sensors (a sensor matrix of less than 160x160 at a dpi of 320 or greater):

- FAR < 0.002%.
- Effective, real world FRR with antispoofing or liveness detection <10%.
- Presentation attack defense measures are a requirement.

Swipe sensors:

- FAR < 0.002%.
- Effective, real world FRR with antispoofing or liveness detection <10%.
- Antispoofing measures are a requirement.

## Facial feature recognition requirements

- FAR < 0.001%.
- TAR > 95%.

## Appendix

The number of comparisons required to verify a particular level of confidence in a claimed FAR is shown below:

`# of Unique Comparisons = C = 1/((1-Conf)) × 1/((FAR))`

where *FAR* is the desired False Accept Rate and *Conf* is the desired Confidence.

For example, with a desired FAR of 0.001%, at a confidence of 96%:

`# of Unique Comparisons = C = 1/((1-0.96)) × 1/((0.00001))`

`C = 25 × 100,000`

`C = 2,500,000`

In this case, 2,500,000 comparisons would be required to reach the desired confidence in the claimed FAR.

To determine the number of unique biometric samples, *n*, to be collected to achieve these comparisons, the formula below can be used:

`# of Comparisons = n!/2(n-2)!`

`C = n(n-1)/2`

`∴ n^2-n = 2C`

where *n* is the number of unique biometric samples.

In the cases where *n*^2>>*n*, the above formula can be simplified to:

`n^2 ≈ 2C`

`∴ n ≈ √2C`

Continuing with the example above, the number of unique biometric samples needed would be:

`n ≈ √(2×2,500,000)`

`n ≈ 2,236.1`

Meaning about 2,237 unique biometric samples will be needed to verify the confidence in the claimed FAR.