1School of Electrical and Computer Engineering, University of Tehran
2Advanced Robotics and Intelligent Systems Lab., Machine Intelligence and Robotics, School of ECE College of Eng. Univ. of Tehran.
3Department of Electrical and Computer Engineering University of Tehran
4Tehran University of Medical Sciences
5Department of Electrical and Computer Engineering, University of Tehran
The ability to determine mood is one of fundamental challenges in affective computing. In this paper, we present a novel approach for mood detection via emotional variations. In this approach, the mood is considered as a low magnitude and more stable, i.e. low frequency, emotion that can be detected using emotion detection approaches. A Bayes classification is applied on a feature vector composed of statistical aspects of the intensity of the emotions. The approach has been implemented in which two emotions, i.e. happiness and sadness, and also neutral state, have been targeted to determine the good, bad, and neutral, mood of subjects respectively. A Bayes classification is applied on a feature vector containing statistical aspects of the intensity of the emotions. The obtained Correct Classification Rate (CCR) is 91.1, with 0.09 mean error and variance of 4.9 discriminating good mood vs. neutral.
Mood, Emotion, Mood Determination, Emotional Features, Face, Non-pathological and Non-clinical mood, Affective computing, Human Computer Interaction