**6. Multibiometric systems**

Some of the limitations imposed by unimodal biometric systems (that is, biometric systems that rely on the evidence of a single biometric trait) can be overcome by using multiple biometric modalities. Increasing the discriminant information and constraints leads to decrease the error in recognition process. More information can be acquired when using different sources of information simultaneously, and the sources of information may be on several types such as multiple biometric traits, algorithms, instances, samples, and sensors. Various scenarios in a multimodal biometric system are demonstrated on **Figure 9**.

Person Identification Using Multimodal Biometrics under Different Challenges http://dx.doi.org/10.5772/intechopen.71667 93

**Figure 9.** Various scenarios in a multimodal biometric system.

communication with each other, where identifying the user does not only require ID but also many other information such as age, gender, interests/hobbies, and language of each user. Knowing the age of the user will help the robot to choose the tone of voice, where child may prefer childish voice tone instead of the manly voice and vice versa. Calling "Mr, Ms, Sir, Madam" when communicating with a person is based on gender, which is also important. Additionally, identifying the interest/hobby of the user will highly enhance the interaction, since it is not acceptable to discuss boxing with a person whose interest is ballet. In addition, communicating with a person using his/her original language ensures promotion of the

**5.2. The most appropriate biometric traits of a person that can easily be identified by** 

Interaction depends on the extent of communication between robots and humans. Human and a robot can construct a communication between each other using several forms. Proximity to each other is the main factor that impacts the communication forms between human and robot. Thus, communication and interaction can be classified into two general

• Remote interaction: the human and the robot are not at the same place and are separated

Choosing biometric traits that robot should use to identify the user should be compatible with the aforementioned interaction categories. For the remote interaction, the biometric traits whose raw features are images such as face, ear, and iris are not convenient choices, since the majority of remote interaction is conducted by voice communication. Therefore, speech recognition may be the best choice, since it is suitable for direct (different room) and mobile calling. For proximate interaction (face-to-face interaction) and in order to create more real interaction, identification process should use a biometric trait that does not require direct contact with the user in order to capture the biometric traits such as face, ear, and voice, which

Some of the limitations imposed by unimodal biometric systems (that is, biometric systems that rely on the evidence of a single biometric trait) can be overcome by using multiple biometric modalities. Increasing the discriminant information and constraints leads to decrease the error in recognition process. More information can be acquired when using different sources of information simultaneously, and the sources of information may be on several types such as multiple biometric traits, algorithms, instances, samples, and sensors. Various

scenarios in a multimodal biometric system are demonstrated on **Figure 9**.

spatially or even temporally (different rooms, countries, or planets)

• Proximate interaction: the humans and the robots are collocated (same room)

interaction**.**

92 Human-Robot Interaction - Theory and Application

categories [27]:

are captured from a far distance.

**6. Multibiometric systems**

**robot**

Consolidating multiple features that are acquired from different biometric sources in order to construct a person recognition system is defined as multibiometric systems. For example, fingerprint and palmprint traits, or right and left iris of an individual, or two different samples of the same ear trait may be fused together to recognize the person more accurate and reliable than unimodal biometric systems. Due to the use of more than one biometric source, many of the limitations of unimodal systems can be overcome by the multimodal biometric systems [28].

Multibiometric systems are able to compensate a shortage of any source using the other source of information. In addition, the difficulty of circumvention of multiple biometric sources simultaneously creates more reliable systems than unimodal systems. On the other hand, the unimodal biometric systems are low cost and require less enrollment and recognition time compared to multimodal systems. Hence, it is essential to carefully analyze the tradeoff between the added cost and the benefits earned when making a business case for the use of multibiometrics in a specific application such as commercial, forensics, and the biometric systems that include large population.

**Identification approach**

Eskandari and Toygar [31]

Farmanbar and Toygar [32]

Hezil and Boukrouche [33]

Ghoualmi et al. [34] Iris

Telgad et al. [35] Face

Patil and Bhalke [36] Fingerprint

Toygar et al. [30] Face

**Biometric traits**

Voice

Iris Face

Palmprint Face

Ear Palmprint

Ear

Fingerprint

Palmprint Iris

**Databases and challenges**

*CASIA-Iris\_Distance:* (I, O, N, D)

*FERET, ORL, BANCA* (*used for weight optimization):* (P, I, E, O, N)

*UBIRIS (used for weight optimization):* (I, O, N)

*FERET:* (P, I, E) *PolyU:* (P)

*IITDelhi-2 Ear IITDelhi Palmprint*

*CASIA IrisV1 USTB 2* (P,I)

*FVC IITD CASIA*

P, pose; I, illumination; E, expression; O, occlusion; N, noise; D, distance.

*XM2VTS:* (P) *BANCA:* (P, I, E, O, N) **Fusion strategy**

Score-level fusion

Featurelevel and Score-level fusion

Featurelevel and Score-level fusion

Feature-Level Fusion

Feature-Level Fusion

fusion

Score-level fusion

*FVC 2004* Score-level

**Table 1.** Comparison of person identification approaches using multimodal biometric traits under different challenges.

**Recognition rate (%)**

http://dx.doi.org/10.5772/intechopen.71667

95

*CASIA-Iris\_Distance:* Face: 92.77 Iris: 77.65 Face + Iris: 98.66

*FERET* ± *PolyU:* Palmprint: 94.30 Face: 83.21

Palmprint: 97.73 Ear: 98.9

Iris: 95.8 Ear: 91.36 Iris + Ear: 99.67

*FVC 2004:* Face-PCA: 92.4

Palmprint + Ear: 100

*CASIA IrisV1* ± *USTB-2*

Fingerprint-Minutiae: 93.05 Fingerprint-Gabor Filter: 95 Face + Fingerprint: 97.5

Fingerprint + Palmprint + Iris = 95.23

*FVC* ± *IITD* ± *CASIA* Fingerprint: 72.73 Plamprint: 65.57 Iris: 80

Palmprint + Face: 99.17

*IITDelhi-2 Ear* ± *IITDelhi Palmprint*

*XM2VTS:* Voice: 78.01 Face: 86.53 Face + Voice: 94.24

Person Identification Using Multimodal Biometrics under Different Challenges

*BANCA:* Voice: 91.54 Face: 92.07 Face + Voice: 97.43

The information used in recognition process can be fused in five different levels [29]:


Among the aforementioned fusion techniques, the most popular ones are score-level fusion and feature-level fusion. Most of the person identification systems use these fusion techniques because of their simplicity and high performance. These systems are compared in **Table 1** by demonstrating many details of the state-of-the-art multibiometric systems.

The results shown in **Table 1** prove that consolidation of different unimodal biometric systems construct a recognition system that is robust against many challenges such as occlusion, pose, and nonuniform illumination. Additionally, the studies presented in **Table 1** demonstrate that score-level fusion of more than one biometric trait overcomes the limitations of unimodal biometric systems, and in most of the studies, score-level fusion results outperform feature-level fusion results for person identification.


P, pose; I, illumination; E, expression; O, occlusion; N, noise; D, distance.

the use of multibiometrics in a specific application such as commercial, forensics, and the

**1.** Sensor level fusion: information of the individual is captured by multiple sensors in order to generate new data that is afterward subjected to feature extraction phase. For instance, in the case of iris biometrics, samples from "Panasonic BM-ET 330" and "LG IrisAccess

**2.** Feature level fusion: in this level, the extracted features from multiple biometric sources are fused to obtain a single feature vector that contains rich biometric information about a client. Integration at feature level is expected to offer good recognition accuracy because it detects the correlated feature values generated by different biometric algorithms, thereby

**3.** Score level fusion: it is the most commonly used fusion technique due to the ease of performing a fusion of the match scores in multibiometric systems. Match scores of multiple classifiers are integrated in score-level fusion to produce a single match score, which is used to get a final decision. Score level fusion requires performing score normalization, which converts the scores into common scale. The fused match score is then calculated by three categories, namely likelyhood ratio–based score fusion, transformation-based score fusion, and classifier-based score fusion.

**4.** Rank level fusion: it is defined as consolidating associated ranks of multiple classifiers in order to derive consensus rank of each identity to establish the final decision. Rank-level fusion provides less information compared to score level fusion, and it is relevant in identification mode. The final decision of rank-level fusion is obtained by three well-known

**5.** Decision level fusion: the outputs (decisions) of different matchers may be fused to obtain a single/final decision (genuine or imposter in a verification system or the identity of the client in an identification system). A single class label can be obtained by employing tech-

Among the aforementioned fusion techniques, the most popular ones are score-level fusion and feature-level fusion. Most of the person identification systems use these fusion techniques because of their simplicity and high performance. These systems are compared in **Table 1** by

The results shown in **Table 1** prove that consolidation of different unimodal biometric systems construct a recognition system that is robust against many challenges such as occlusion, pose, and nonuniform illumination. Additionally, the studies presented in **Table 1** demonstrate that score-level fusion of more than one biometric trait overcomes the limitations of unimodal biometric systems, and in most of the studies, score-level fusion results outperform

methods namely Highest Rank, Borda Count, and Logistic Regression methods.

niques like majority voting, behavior knowledge space, etc.

feature-level fusion results for person identification.

demonstrating many details of the state-of-the-art multibiometric systems.

The information used in recognition process can be fused in five different levels [29]:

biometric systems that include large population.

94 Human-Robot Interaction - Theory and Application

4000" sensors may be fused to obtain one sample.

identifying a set of distinguished features.

**Table 1.** Comparison of person identification approaches using multimodal biometric traits under different challenges.
