*3.6.1. Advantages and disadvantages of voice recognition*

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, entertainment, etc.

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 individual's voice and poor accuracy performance of a typical speech-based authentication system. Existing techniques are able to reduce variability caused by additive noise or linear distortions, as well as compensating slowly varying linear channels [14].

are wrongly mismatched and FAR refers to the expected probability that two non-mate

Person Identification Using Multimodal Biometrics under Different Challenges

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

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Single-valued measure "Equal Error Rate (EER)," that is threshold independent, can also be used to evaluate the performance of recognition systems. EER is the value, where FRR and

Detection Error Trade-off (DET) or Receiver Operating Characteristic (ROC) curves are also used to compare the performance of biometric systems in which both curves plot FRR against

There are several challenges and key factors that can significantly affect the recognition performance as well as degrading the extraction of robust and discriminant features. Some of these challenges such as pose, illumination, aging, facial expression variations, and occlusions

**Figure 7.** The challenges in the context of face recognition: (a) pose variations, (b) illumination variations, (c) aging

are briefly described below, and these challenges are illustrated in **Figure 7**.

samples are incorrectly matched.

**4. Biometric challenges**

variations, (d) facial expressions, (e) occlusions.

FAR in the normal deviate and linear scale, respectively.

FAR are equal.
