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

**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

**2.** Illumination variation: when the image is captured, it may be affected by many factors to some degree. The appearance of the human face or ear is affected by factors such as lighting that includes spectra, source distribution, and intensity and also camera characteristics such as sensor response and lenses. Illumination variations can also have an effect on the appearance because of skin reflectance properties and the internal camera control [21]. The problem of illumination variation is considered to be one of the main technical challenges in biometric systems especially for face and ear traits, where the face of a person can appear dramatically different as shown in **Figure 7b**. In order to handle variations in lighting conditions or pose, an image relighting technique based on pose-robust albedo estimation [22] can be used to generate multiple frontal images of the same person with

**3.** Aging: aging can be a natural cause of age progression and an artificial cause of using makeup tools. Facial appearance changes more drastically at younger ages less than 18 years due to the change in subject's weight or stiffness of skin. All aging related variations such as wrinkles, speckles, skin tone, and shape degrade face recognition performance. One of the main reasons for the small number of studies concerning face recognition in the context of age factor was the absence of a public domain database for studying the effect of aging [23], since it was very difficult to collect a dataset for face images that contains images for the same subject taken at different ages along his/her life. An example set of images for differ-

**4.** Occlusion: faces may be partially occluded by other objects such as scarf, hat, spectacles, beard, and mustache as shown in **Figure 7e**. This makes the face detection process a difficult task and the recognition itself might be difficult because of some hidden facial parts making features hard to be recognized. For these reasons, in surveillance and commercial applications, face recognition engines reject the images when some part of it is not detected. In the literature, local-feature based methods have been proposed to overcome these occlusion problems [24]. On the other hand, the iris could potentially be occluded due to the eyelashes, eyelids, shadows, or specular reflections, and these occlusions can lead to

**5.** Facial expression: the appearance of faces is directly affected by a person's facial expression such as anger, surprise, and disgust as shown in **Figure 7d**. Additionally, facial hair such as beard and mustache can change facial appearance specifically near the mouth and

ent ages of the same person is presented in **Figure 7c**.

[18–20].

90 Human-Robot Interaction - Theory and Application

variable lighting.

higher false non-match rates.

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 **Figure 8**.
