**1. Introduction**

Biometrics is a technology that uses physical and/or behavioral characteristics of people to identify them. Systems of this type implement two processes (**Figure 1**) [1]:


The physical features are fingerprints, hand geometry, handprint, facial image, iris, retina, and ear. Behavioral features are signature, lip motion, speech, dynamics of typing, hand movements, and gait.

The characteristics of effective biometrics are:

1. Unique features for each individual

of sensitivity (t). This factor indicates the optimal sensitivity threshold at which the same number of people is incorrectly rejected and incorrectly accepted. The lower the EER error value, the better the biometric system is.

The FMR (FRR) and FNMR (FAR) parameters can also be represented by

• Receiver operating characteristic (ROC) curve showing the dependence of FNMR on FMR. You can use it to show the accuracy of the system.

*The graph of FAR, FRR, and EER in receiver operating characteristic (ROC) curve.*

identified

possible

intra-class variance

Distorted biometric data may prevent the correct alignment process with database templates, as a result of which users are incorrectly rejected or

from the data used to generate the template during registration, thus affecting the matching process. The biometric template should have a small

should ensure small similarities between classes in the feature space. There is an upper limit to the users who can be effectively distinguished by any biometric system. The capacity of the identification system cannot be arbitrarily increased for fixed sets of feature vectors and the matching algorithm. The biometric template should have large interclass variations

recognition. It is also possible to create artificial biometric patterns in order

Intra class variations Biometric data obtained from the person during authentication may differ

Interclass similarities Biometric features should be significantly different for different people and

Non-universality Obtaining accurate (useful) biometric data from the users is not always

to accept the identity of another person

Intruder attacks Attacks of this type involve the manipulation of biometric features to avoid

**Name Description**

Distortion of the input biometric data

graphs (**Figure 2**):

*Multimodal Biometrics for Person Authentication DOI: http://dx.doi.org/10.5772/intechopen.85003*

**Figure 2.**

**Table 1.**

**179**

*Limitations of unimodal biometrics.*

**Figure 1.** *Biometric recognition system.*

2. Invariant traits over time (e.g., due to the effect of aging)

3. Features that are relatively easy to obtain (computational complexity small)


The security of the biometric system is usually assessed on the basis of some indicators. These are:


of sensitivity (t). This factor indicates the optimal sensitivity threshold at which the same number of people is incorrectly rejected and incorrectly accepted. The lower the EER error value, the better the biometric system is.

The FMR (FRR) and FNMR (FAR) parameters can also be represented by graphs (**Figure 2**):

• Receiver operating characteristic (ROC) curve showing the dependence of FNMR on FMR. You can use it to show the accuracy of the system.

### **Figure 2.**

2. Invariant traits over time (e.g., due to the effect of aging)

*Security and Privacy From a Legal, Ethical, and Technical Perspective*

4.Precise algorithms enabling classification

5. Resistance to various types of attacks

6.Low cost

**Figure 1.**

*Biometric recognition system.*

indicators. These are:

in the database.

**178**

7. Ease of implementation

3. Features that are relatively easy to obtain (computational complexity small)

The security of the biometric system is usually assessed on the basis of some

• False match rate (FMR). It belongs to the group of matching errors. This indicator is defined as the expected probability that the downloaded sample will be falsely matched to the template in the database, but it will not be the test user pattern. If the indicator is high, it means that there is a risk that an

• False rejection (FRR) is equivalent to the FMR. The difference between these indicators is that FMR refers to a single match, and the FRR refers to a situation where one or more attempts to match a sample to a template from the database

• False discrepancy (FNMR). This is the coefficient determining the probability that the sample taken will not be matched to the pattern in the database belonging to the user from whom the sample was taken. In biometric verification (1:1) systems, the indicator means that the sample has not been identified by a specific pattern, while in biometric identification systems (1:N), this indicator determines the probability that a given pattern will not be found

• The false acceptance factor (FAR) is equivalent to the FNMR indicator. The difference between him and FNMR is the same as between FRR and FMR.

• Equal error rate (EER). It is defined as the intersection of the FAR and FRR characteristics in the graph of the dependence of these errors on the threshold

may occur. The FRR error is referred to in the literature as type I error.

unauthorized person will be recognized as a system user.

*The graph of FAR, FRR, and EER in receiver operating characteristic (ROC) curve.*


### **Table 1.**

*Limitations of unimodal biometrics.*


multiple templates of the same biometric method obtained with the help of a single sensor, and (e) a multimodal system combining information about the biometric

In multimodal biometric systems, there are a number of strategies (scenarios)

• Data fusion from sensors. Data from various sensors form one vector. Fusion of information obtained from many different sensors for a single biometric

• The fusion of feature vectors extracted from various biometric modalities for further processing. A merger of information obtained from several unimodal biometric systems that process different body characteristics of the same

• Fusion at the decision level. The merger of decisions developed on the basis of information from different biometric modalities, and the resultant feature vector defines two main classes, i.e., rejection or acceptance (**Figure 4b**).

features of the individual to establish his identity [2–4].

for the fusion of biometric information:

*Multimodal Biometrics for Person Authentication DOI: http://dx.doi.org/10.5772/intechopen.85003*

**2.2 Fusion levels**

feature.

**Figure 4.**

**181**

*Levels of fusion. (a) Feature level fusion and (b) score/rank level fusion.*

person (**Figure 4a**).
