**4.2 Evaluation mode**

There are three modes of performance evaluations, which are technology, scenario, and operational evaluation. When evaluating biometric algorithms, technological evaluations are the most common and often the most feasible. Since this type of evaluation is done using saved samples, the results are reproducible, and the evaluation is not a tedious or complicated process.


The biggest disadvantage of technological evaluations is that they do not necessarily reflect the final conditions of use of the system. For this reason, it is important to collect a set of samples of the conditions of use of the target system when preparing an assessment.

## **4.3 Database**

Registered samples used in technology assessments are collected in databases. Data collection is performed using a group of volunteers, at least some of whom provide multiple acquisitions of the same biometric modality (e.g., the same finger) to have relevant attempts. To make collection efficient, samples of several objects can be collected from each volunteer, for example, every ten fingers. The characteristics of the database have a great impact on the results of an evaluation. As previously stated, with the exception of the capabilities of the biometric algorithm, the amount of available information can be used to characterize the objects.

#### **4.4 Degree of confidence**

To be able to make an assertion about the FRR 1% @ FAR 1 / 1 000 000 (i.e., when the system operates in a mode where one out of one million impostor attempts

**Figure 5.** *DET graph sample.*

is-falsely-considered a match, one percent of the genuine attempts would fail) it at least one million impostor attempts (user sticking perfectly to another person's template). It is not difficult to understand that the uncertainty of such an assertion would be rather high. The result depends heavily on how the two most similar samples in the database are scored. When comparing and viewing a DET (detection error trade-off) graph, it is important to understand that the uncertainty is higher on the side of the edges of the image. The number of comparisons made is only an important factor affecting confidence. The key to getting better statistical significance is to make as many uncorrelated attempts as possible.
