Human Emotion Recognition from Body Posture with Machine Learning Techniques

Recently, increasing attention in the field of gesture recognition, has become a key strategy in analyzing the emotional states of human body movements for social communication. Most real-life scenarios include identifying emotions from facial expressions, vocal synthesis, hand recognition and body gestures. The body posture powerfully conveys the micro emotions of a person in depth. The prediction of human - gait is significantly harder, because the pattern of the human pose estimation has additional degrees of self-determination than the facial emotions, and the overall shape varies robustly during the articulated motion. In this paper, we propose a novel method to recognize 17 different micro emotions from GEMEP dataset based on human upper body gestures dynamics features extracted from the abstract representations of patterns from videos. In the experimental results, KNN exhibit the proposed architecture's effectiveness with an accuracy rate of 97.1% for the GEMEP dataset, 95.2% for SVM, 51.6% for Decision Tree and 49.7% Naive Bayes, respectively.