A Supervised Approach to Support the Analysis and the Classification of Non Verbal Humans Communications

Background: It is well known that non verbal communication is sometimes more useful and robust than verbal one in understanding sincere emotions by means of spontaneous body gestures and facial expressions analysis acquired from video sequences. At the same time, the automatic or semi-automatic procedure to segment a human from a video stream and then figure out several features to address a robust supervised classification is still a relevant field of interest in computer vision and intelligent data analysis algorithms. Materials and Methods: We obtained data from four datasets: first dataset contains 100 images of humans silhouettes (or templates) acquired from a video sequence dataset, second dataset contains 543 images of gestures from a preregistered video of MotoGp driver Jorge Lorenzo, the third one 200 images of mouths and finally the fourth one 100 images of noses; third and fourth datasets contain images acquired by a tool implemented from the authors and also samples available in literature in public databases. We used supervised methods to train the proposed classifiers and, in particular, three different EBP Neural-Network architectures for humans templates, mouths and noses and J48 algorithm for gestures. Results: We obtained on average a 80% correct classification for binary classifier of humans templates (no false positives), 90% correct classification for happy/non happy emotion, 85% of binary disgust/non disgust emotion and 80% correct classification related to the 4 different gestures.