**4. Biometric challenges**

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

Speech recognition process starts by acquiring the sound from a user using microphone, and then, the series of acoustic signals are converted to a set of identifying words. The speech recognition depends on many factors such as language model, vocabulary size, speaking style, speaker enrollment, and transducer [15]. Speech recognition system is classified to "speaker dependent system," if the user should train the system before using it, and to "speaker independent system," if the system can recognize any speaker's speech without the need to train phase. Speech recognition systems can also be divided into "isolated word speech" or "continuous speech" based on the number of the used vocabularies

Speaker models [16, 17] enable us to generate the scores from which we will make decisions. As in any pattern recognition problem, the choices are numerous, and the most popular and dominated technique in last two decade is Hidden Markov Models. There are also other techniques used for speech recognition systems such as Artificial Neural Networks (ANN), Back Propagation Algorithm (BPA), Fast Fourier Transform (FFT), Learn Vector Quantization (LVQ), and Neural Networks (NN). A typical speech recognition system is

Different measurements can be used to evaluate the performance of biometric systems. The most famous measurement is the recognition rate, which is defined as the percentage of the samples that are correctly matched samples to the total tested samples. Another popular measurement is False Reject Rate (FRR) versus False Accept Rate (FAR) at various threshold values, where FRR refers to the expected probability for two mate samples which

distortions, as well as compensating slowly varying linear channels [14].

*3.6.2. Speech recognition*

88 Human-Robot Interaction - Theory and Application

for identification process.

shown in **Figure 6**.

**3.7. Performance evaluation of biometrics systems**

**Figure 6.** Block diagram of a speech recognition system.

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

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

**1.** Pose variation: the images of a face or ear vary because of the camera pose (different viewpoints) as shown in **Figure 7a**. In this condition, some facial parts such as the eyes or nose may become partially or fully occluded. Pose variation has more influence on recognition process because of introducing projective deformations and self-occlusion. Thus, it is possible that images of the same person taken from two different poses may appear more different (intra-user variation) than images of two different people taken with the same poses (inter-user variation). There are many studies that deal with pose variation challenges in [18–20].

chin regions. Moreover, facial expression causes large intra-class variations. In order to handle these facial expression problems, local-feature-based approaches and 3D-model-

Person Identification Using Multimodal Biometrics under Different Challenges

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Human-robot interaction (HRI) is the study of how people can interact with robots and to what extent robots are exploited and used for successful interaction with human beings. It could also be defined as a field of study dedicated to understanding, designing, and evaluating robotic systems for use by or with humans. In general, the interaction is based on the communication with or reaction to each other, either people or things as shown in

**5.1. The importance and the role of person identification in human-robot interaction**

Person identification is a very important function for robots, which work with humans in the real world [26]. Human identification by robot may enhance the extent of interaction and

based approaches are designed [25].

**5. Human robot interaction (HRI)**

**Figure 8.** Block diagram of a human-robot interaction system.

**Figure 8**.


chin regions. Moreover, facial expression causes large intra-class variations. In order to handle these facial expression problems, local-feature-based approaches and 3D-modelbased approaches are designed [25].
