**3.1. Face**

human identification were classified into two main approaches as traditional and biometrics approaches. Matching process of these methods is conducted not only by humans but also by automated systems, which speed up the matching process in addition to the capability of the

The traditional human identification approaches depend on changeable parameters such as passwords or magnetic/ID cards. These parameters can be easily used by illegal persons, if they know the password or have the card. Losing, forgetting, or stealing are common disadvantages for all the traditional identification methods which make it unreliable and inaccurate especially in the high precise system such as forensics, financial, bank, and border ports systems. The need for more robust systems of person identification in addition to the development of the sensors and automated systems was incentive to construct the systems that depend on the unique features of each person. These features are extracted from a human trait such as fingerprint, face, and speech. Human recognition using features that are extracted from inherent physical or behavioral traits of the individuals is defined as biometrics. In addition to the enhancement of the efficiency and capability of recognition systems, biometrics facilitates identifying, and claiming process, where it is not required to memorize any passwords or to carry any ID cards such as pass-

Biometrics is the science of establishing the identity of an individual based on a vector of features derived from a behavioral characteristics or specific physical attribute that the person holds. The behavioral characteristic includes how the person interacts and moves, such as their speaking style, hand gestures, signature, etc. The physiological category includes the physical human traits such as fingerprints, iris, face, veins, eyes, hand shape, palmprint, and many more. Evaluating these traits assists the recognition process using the biometric

A biometric system includes two main phases as enrollment and recognition. Biometric data (image, video, or speech) are captured and stored in a database in enrollment phase. The recognition phase mainly includes extraction of the salient features and generation of the matching scores in order to compare query features against the stored templates. The biometric system will report an identity at the end of the decision process after performing matching, and this will be the identity of the most resembling person in the

In this section, a brief overview, requirements, advantages, and disadvantages of the most

commonly used unimodal biometric traits are presented and explained.

**2. Person identification approaches (traditional vs. biometrics)**

large size of memory.

82 Human-Robot Interaction - Theory and Application

ports or driving license.

systems [1].

database.

**3. Common biometric traits**

Face recognition is one of the most important abilities that we use in our daily lives. Face recognition has been an active research area over the last 40 years, and the first automated face recognition system was developed by Takeo Kanade in 1973 [2]. The increasing interest in the face recognition research is caused by the satisfactory performance in many widely used applications such as the public security, commercial, and multimedia data management applications that use face as biometric trait. Face recognition has several advantages over other biometrics such as fingerprint and iris besides being natural and nonintrusive. First, the most important advantage of face is that it can be captured at a distance and in covert manner. Second, in addition to the identity, the face can also show the expression and emotion of the individual such as sadness, wonder, or scaring. Moreover, it provides a biographic data such as gender and age. Third, large databases of face images are already available, where the users should provide their face image in order to acquire driver's license or ID card. Finally, people are generally more willing to share their face images in the public domain as evinced by the increasing interest in social media applications (e.g., Facebook) with functionalities like face tagging.

A face recognition system generally consists of four modules namely face detection, preprocessing, feature extraction, and matching as shown in **Figure 1**. An original face image and its preprocessed variant are also shown in **Figure 2**.
