## 1 Introduction  
Facial expressions [1] are a crucial non-verbal method of indicating meaning and represent a unique, universal way for people to communicate. The facial expression recognition system (FERS) is a contactless recognition system in which the image of a human face of a person can be captured from a distance without any intervention or interruption, even when he/she is moving around, walking, sitting, or performing activities [2]. Facial expressions play an essential role in our daily communication with people and in social interactions [3]. The FERS is mainly used to identify types of human facial expression [4]. According to [PERSON] et al. [5], there are six basic expressions, including a neutral face as a baseline reference. Figure 1 shows some examples of human facial expressions, e.g., fear (FA), anger (AN), disgust (DI), happy (HA), neutral (NE), sad (SA), and surprise (SU). The FERS [6] is an emergent research topic in computer vision research areas. It has comprehensive potential in a diversified field of applications [7] with various challenges in healthcare, education, marketing research, business organization, customer and retail fields, government, entertainment, and within the Internet of Things (IoT). There exists several vendors such as Microsoft, IBM, Amazon, and Google that provide some application programming interfaces using facial expressions but with limited solutions. Here, these diversified fields of application for FERS are discussed in terms of the four major concerns, which are as follows:  
* _e-Healthcare_- The real-time FERS incorporates the healthcare system via the system that is used to analyze and detect the image's visualization of the patient's feelings remotely by identifying facial expressions [8] for patients with different ages, puberty levels, and genders collected from a giant cloud and from social networks. The m-Health provides mobile device-based practices to patients to support their medicine administration and daily healthcare facilities. e-Health is an electronic health service that uses information and communication technology for delivering facilities digitally and for processing patients and doctors through computers for drug administration. Both e-Health and m-Health provide immense support to healthcare industries in building e-Healthcare systems to ensure that patients, doctors, medical professionals, and businesses benefit, as well as to ensure the establishment of a healthy civilization with technological advancements in smart cities. Electronic healthcare systems provide services to patients to physically localize and monitor through recognizing their voice, speech, gesture movement, and facial expressions. Our proposed facial expression recognition system (FERS) will improve the services of healthcare systems. It is a significant challenge to obtain good results in the context of more efficient and less costly health services. Hence, while integrating the FERS into the healthcare framework, all healthcare requirements, such as automated intelligent sensors, sophisticated tools, security, authenticity, access, and privacy, should also be considered. * _Social IoT_- Social IoT systems represent an evolution of IoT-based systems. It establishes a platform for interconnecting subjects or objects worldwide through social relationships. It provides better services to users by relaxing the common interests between the users. Now, the services of social IoT are exploited in emotion-recognition as these emotions relate to the social activities of humans in their daily life. Hence, the integration of social IoT services will make life easier with several social care facilities for people [9]. The proposed FERS is useful for developing IoT-based smart devices and appliances. It can be used for several entities such as education, marketing research, retail, government, media and content, gaming, and finance. During online teaching, the facial expressions of students can be compared to their interest and understanding of topics that have been taught to them.