**3.3 Biometric systems**

A biometric system is a system that allows the recognition of a certain characteristic of an individual using mathematical algorithms and biometric data. There are several uses of biometric systems. There are systems that require enrollment upstream of users. Other identification systems do not require this phase.


**Figure 4** presents the architecture of a biometric system, which consists of the following elements:


#### **Figure 3.**

*Distribution of the global biometric market.*

**Figure 4.** *Biometric system architecture.*


#### **4. Performance of biometric systems**

#### **4.1 Performance evaluation**

For the evaluation of the precision of a biometric system, which makes it possible to measure these performances, numerous attempts have been made on the system, and all the similarity scores are saved.

By applying the variable score threshold to similarity scores, the pairs of false recognition rate (FRR) and false acceptance rate (FAR) can be calculated. The false recognition rate, or FRR, is the measure of the likelihood that the biometric system will incorrectly reject an access attempt by an authorized user. It is stated as the ratio of the number of false recognitions divided by the number of identification

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*Biometric Systems and Their Applications DOI: http://dx.doi.org/10.5772/intechopen.84845*

ers of ten (10*<sup>p</sup>*

2

**4.2 Evaluation mode**

acquired fingerprints

simulated environment

population

preparing an assessment.

**4.4 Degree of confidence**

**4.3 Database**

divided by the number of identification attempts.

edly in different modes to get other FRR/FAR pairs.

evaluation is not a tedious or complicated process.

attempts. On the other hand, the false acceptance rate, or FAR, is the measure of the likelihood that the biometric system will incorrectly accept an access attempt by an unauthorized user. It is stated as the ratio of the number of false acceptances

The results are presented either as such pairs, i.e., FRR at a certain level of FAR or as the graph in **Figure 5**. The rates can be expressed in several ways, for example, in percentages (1%), in fractions (1/100), in decimal format (0.01), or using pow-

FRR equal to FAR level. Some systems do not report the similarity score, only the decision. In this case, it is only possible to win a single FRR/FAR pair (and not a continuous series) as a result of a performance evaluation. If the mode of operation (the security level) is adjustable (i.e., we have a means of controlling the scoring threshold used internally), the performance evaluation can be performed repeat-

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

• Technological evaluation: Evaluation using recorded data, e.g., previously

• Scenario evaluation: End-to-end evaluation of the system using a prototype or

• Operational evaluation: Evaluation in which the performance of a complete biometric system is determined in an application environment with a specific

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

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

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

amount of available information can be used to characterize the objects.

). When comparing two systems, the most accurate shows a lower

#### *Biometric Systems and Their Applications DOI: http://dx.doi.org/10.5772/intechopen.84845*

*Visual Impairment and Blindness - What We Know and What We Have to Know*

• The storage module that contains the biometric templates of the system enrollees.

• The matching module that compares the data extracted by the extraction module with the data of the registered models and determines the degree of

• The decision module that determines whether the similarity index returns through the matching module is sufficient to make a decision about the iden-

For the evaluation of the precision of a biometric system, which makes it possible to measure these performances, numerous attempts have been made on the

By applying the variable score threshold to similarity scores, the pairs of false recognition rate (FRR) and false acceptance rate (FAR) can be calculated. The false recognition rate, or FRR, is the measure of the likelihood that the biometric system will incorrectly reject an access attempt by an authorized user. It is stated as the ratio of the number of false recognitions divided by the number of identification

similarity between the two biometric data.

tity of an individual.

**Figure 4.**

**Figure 3.**

*Distribution of the global biometric market.*

*Biometric system architecture.*

**4.1 Performance evaluation**

**4. Performance of biometric systems**

system, and all the similarity scores are saved.

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attempts. On the other hand, the false acceptance rate, or FAR, is the measure of the likelihood that the biometric system will incorrectly accept an access attempt by an unauthorized user. It is stated as the ratio of the number of false acceptances divided by the number of identification attempts.

The results are presented either as such pairs, i.e., FRR at a certain level of FAR or as the graph in **Figure 5**. The rates can be expressed in several ways, for example, in percentages (1%), in fractions (1/100), in decimal format (0.01), or using powers of ten (10*<sup>p</sup>* 2 ). When comparing two systems, the most accurate shows a lower FRR equal to FAR level. Some systems do not report the similarity score, only the decision. In this case, it is only possible to win a single FRR/FAR pair (and not a continuous series) as a result of a performance evaluation. If the mode of operation (the security level) is adjustable (i.e., we have a means of controlling the scoring threshold used internally), the performance evaluation can be performed repeatedly in different modes to get other FRR/FAR pairs.
