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

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. We obtained data from four datasets and 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. We obtained on average of correct classification equal to a: 80% for binary classifier of humans templates, 90% for happy/non happy, 85% of binary disgust/non disgust and 80% related to the 4 different gestures.