Deep Learning Based Approach of Emotion Detection and Grading System

In the past two decades, there has been an increasing research in recognition and analysis of human emotions in areas of neuroscience, psychology, cognitive science, and computer science. Facial expressions play an important role in assessment of several neuropsychiatric disorders where patients are having impairment in recognition as well as expression of emotional facial expressivity. The majority of the previous work in the domain of machine analysis of human emotion was focused on recognition of prototypic expressions of six basic emotions or also on finding out movements of the individual muscle that the face can produce. Most of these automated facial expression recognition methods were based on data that has been posed on demand and acquired in laboratory settings. But, for real-world applications, instead of classifying a face image into one of the facial expression categories, focus needs to be given on the problem of estimating the depth of facial expression for further grading the emotions leading to applicability in various clinical research. In this paper we have proposed a deep learning-based Emotion Detection and Grading System (D-EDGS) for estimation of facial emotional expression into one of the seven basic categories of emotion. Along with this the proposed model D-EDGS also works towards grading the emotions into low, medium and high scale. In future, effectiveness of D-EDGS can be applied for the development of decision support system in assessment of neuro psychiatric disorders.