Mobile Customers’ Charter with Mood Meter

Abstract

Many studies have shown that facial expressions are vital for human-to-human interaction, and they are considered one of the most important cues in the psychology of emotion. People express their feelings mostly through visual, facial, and bodily expressions. Consequently, facial expression recognition is the primary indicator of a human affective state. Thus, this research project aims to explore the usage of desktop devices to detect emotion from the face of the customers and determine their mood. The system will use the basic technique in seeing calm and annoyed faces in the pictures implemented as a desktop application. This research aims to monitor the services rendered by the organization's employees by determining the mood of customers if they are satisfied with their service. Simultaneously, the system will also help managers or owners know the current status of their business. With the help of OpenCV and emgucv, face detection can be implemented in the desktop application through webcam. Moreover, the haar cascade object detection helped the researchers train the system to detect two moods (calm and annoyed) by comparing the captured picture to the database’s images. However, facial expression detection accuracy greatly depends on the clear images the camera captures. The lightings and distance of the person in the image should be considered. Therefore, training the system in comparing images to the database is really important to achieve accuracy in face and mood detection.