Evaluating Instantaneous Psychological Stress from Emotional Composition of a Facial Expression

Abstract

Human psychological stress is a vast and highly complicated topic of study and research. The types and kinds of stress observed in humans vary among researchers. Also, to identify stress, many methods exist. Most of these methods are non-intrusive and are based on self-reporting and questionnaires which reduces the real-time efficacy of the procedure. Intrusive methods are, on the other hand, time consuming and cumbersome. The total problem of non-intrusive psychological stress detection from facial images can be visualized in three incremental stages: instantaneous analysis of subject, historical analysis of subject, and the subject’s environmental analysis. In this paper, we deal with instantaneous analysis of a subject. This means that the stress behavior of a subject is predicted for one moment of time using an image of his/her facial expression. In order to do so, we have conducted two surveys to establish the relationship between emotional compositions of a facial expression with stress and also to establish the relationship of individual emotions with stress. The novelty of the paper is 1) to establish relationships between the seven basic emotions (anger, contempt, disgust, fear, happy, sad, and surprise) and stress, 2) to establish relationship between emotional composition of a facial expression and stress, and 3) to predict a formula for evaluating stress in terms of emotional percentage mixture of a facial expression. In order to achieve the three goals, we use Facial Action Unit (AU) [1] coded image data to predict the emotional mixture of the facial expression in terms of the seven basic emotion percentages. An AU represents one of the many basic muscle movements that make up the facial expression. Then we analyze the survey outcomes to establish the relationship between individual emotions and stress. Finally we correlate the survey outcomes with the emotional mixture data obtained from the facial expression using Hidden Markov Model (HMM) approach to both establish a relationship of emotional composition with stress and to predict a formula for stress in terms of the seven basic emotion percentages jointly.