**3.4. Fingerprint**

The modern history of fingerprint identification begins in the 19th century with the development of identification bureaus charged with keeping accurate records about indexed individuals. The acquisition of fingerprint was performed firstly by using ink technique [7].

that the recognition rate is not affected by aging [10]. Third, ear biometric is convenient as its acquisition is easy because the size of the ear is larger than fingerprint, iris, and retina and smaller than face. Ear data can also be captured even without the knowledge or cooperation of the user from far distance [11]; therefore, it can be used in passive environment. This makes ear recognition especially interesting for smart surveillance tasks and for forensic image analysis, because ear images can typically be extracted from profile

Person Identification Using Multimodal Biometrics under Different Challenges

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

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The main drawback of ear biometric is occlusion, where the ear can be partially or fully covered by hair or by other items such as head dress, hearing aids, jewelry, or headphone. In an active identification system, it is not a critical point as the subject can pull his or her hair back, but in a passive identification, it is a problem as there will be nobody informing the subject. Other challenges on ears are different poses (angles), left and right rotation, and different

The activities of automatic speaker verification and identification have a long history going back to the early 1960s [12]. Dragon systems were the early applications that were used as speech recognizer [13], which focused on the ability of recognition system to provide acoustic knowledge about speaker. Baum-Welch HMM procedures were employed by these systems

Speech or voice is one of the behavioral traits that can be used in biometric systems to identify the user based on the stored voice in the enrollment phase, where the voice characteristics such as pronunciation style and voice texture are unique and distinctive for each person. On the other hand, voice can also be considered physiological in addition to behavioral feature

Generally, voice recognition is nonintrusive, and people are willing to accept a speech-based biometric system with as little inconvenience as possible. It also offers a cheap recognition technology, because general purpose voice recorders can be used to acquire the data. However, a person's voice can be easily recorded and can be used for authorized access, and the noise can be canceled by specific software. As a result, these make speech recognition to be used in many applications such as financial applications, security, retail, crime investigation,

Speech-based features are sensitive to a number of factors such as background noise, room reverberation, the channel through which the speech is acquired (such as cellular, land-line, and VoIP), overlapping speech, and Lombard or hyper-articulated speech. Additionally, the emotional and physical state of the speaker are important. An illness such as flu can change a person's voice, and it makes voice recognition difficult. Speech-based authentication is currently restricted to low-security applications because of high variability in an

head shots or video footage.

lighting conditions.

**3.6. Speech**

to train models.

entertainment, etc.

based on the shape of the vocal track.

*3.6.1. Advantages and disadvantages of voice recognition*

The main application of fingerprint identification is forensic investigation of crimes. John Maloy performed a forensic identification in the late 1850s [8] by designing a high-security identification system that has always been the main goal in the security business.

The main reasons for the popularity of fingerprint recognition are as follows:


A typical fingerprint feature called minutiae is extracted from fingerprint images, as shown in **Figure 5**, and used for matching process for a fingerprint recognition system.

#### **3.5. Ear**

Recognizing people by their ear has recently received significant attention in the literature. There are many factors that made ear a widely used biometrics. First, the shape of the ear and the structure of cartilaginous tissue of the pinna are very discriminate. It is formed by the outer helix, the antihelix, the lobe, the tragus, the antitragus, and the concha. The ear recognition approaches are based on matching the distance of salient points on the pinna from a landmark location. Second, ear has a structure which does not vary with facial expressions or time, and it is very stable for the end of life. It has been shown

**Figure 5.** A typical minutiae feature extraction algorithm [9].

that the recognition rate is not affected by aging [10]. Third, ear biometric is convenient as its acquisition is easy because the size of the ear is larger than fingerprint, iris, and retina and smaller than face. Ear data can also be captured even without the knowledge or cooperation of the user from far distance [11]; therefore, it can be used in passive environment. This makes ear recognition especially interesting for smart surveillance tasks and for forensic image analysis, because ear images can typically be extracted from profile head shots or video footage.

The main drawback of ear biometric is occlusion, where the ear can be partially or fully covered by hair or by other items such as head dress, hearing aids, jewelry, or headphone. In an active identification system, it is not a critical point as the subject can pull his or her hair back, but in a passive identification, it is a problem as there will be nobody informing the subject. Other challenges on ears are different poses (angles), left and right rotation, and different lighting conditions.
