AI-based Facial Recognition in Emotional Recognitions AMIR DIRIN Haaga-Helia University of Applied Sciences, Helsinki, Finland, firstname.lastname@example.org JYRKI SUOMALA Laurea University of Applied Science, Espoo, Finland ARI ALAMÄKI Haaga-Helia University of Applied Sciences, Helsinki, Finland
Facial recognition is an approach to recognize a human face with the help of computer vision. The popularity of smart gadgets and advancement on the cameras capabilities have caused the concept of facial recognition to become a hot topic among academician and practitioners. Besides the tradition facial recognition in the surveillance system, commercial facial recognition system to measures emotions have nowadays become popular. These systems are often AI-based and use facial recognitions algorithms along with biometrics to map face features from an image or through a livestream to identify the motions. The aim of this paper is to study the credibility of these systems to detect emotion accurately. Humans have complex personalities and the personality often express in our facial expressions which is not necessary reflected to the emotion. For example, personal disorders such as narcissistic personal or histrionic personality disorder have different facial expressions than persons who have not been diagnosis with any disorders. The facial expressions of those persons are not representations of emotions that will be detected through the diagnostic systems. Therefore, the complement technologies and solutions are needed to make the measurement more accurate.
Facial recognition based emotional measurements devices have become very popular specifically as a supplementary for usability and user experience measurement. In addition, many companies have promoting their facial recognition solution to promote sales and improve customer relationships . The advancement of these devices mainly based on the significant improvements on related technologies such as HD based camera and facial recognition algorithms such as Fisherfaces , Local Binary Patterns Histograms (LBPH) , Deep Neural Network (DNW) , Rectified Linear Units Layer (ReLU) , and Convolutional Neural Network (CNN) . These algorithms are widely used by industries for their facial and emotional recognitions. The facial recognition’s application getting very popular and increasing in the industries as well as in consumer level. The facial recognitions’ based solutions have become very available even among children for example, Snapchat, which is based on computer vision, google search engine use the widely the pattern recognitions, or Facebook, which detect the face on the picture . These examples combine the artificial intelligent approaches and computer visions  to teach the algorithm to make more accurate measurements. Many products such as iMotions, FaceReader, Deepface, pursue to measure the emotion through the facial recognitions. The emotion’s measurement can be achieved by three main approaches, subjective, behavioral, and physiological approaches . Behavioral measurements cover many approaches for measuring user behavior, for example, Facial Action Coding System (FACS)  and , which measures facial poses. Physiological measurements allow to measure emotions change, for example, autonomic nervous system  or detecting galvanic skin response via a sensor. The purpose of this paper is to investigate the efficiency of the latest facial recognitions based emotional detection. This study is based on literature review in which we argue and demonstrate that human personality impact on the facial expression. The result of this study helps the practitioner to learn about the reliability and for academician a further research topic on AI based facial recognitions.