Recognition and Classification of Facial Expressions using Artificial Neural Networks

This paper addresses the problems of recognition and the classification of the facial expressions from videos. Currently there are excellent results focusing on the control environments, where artificial facial expressions are found. It is by far the largest database of facial expression, valence, and arousal in the wild enabling research in automated facial expression recognition in two different emotion models. On the other hand, much remains to be improved when it comes to the uncontrolled environments, in which variations in lighting, camera angle, face framing, make the small amount of labelled data available in impediment when the training models of automated learning. In order to attack this difficulty, the Reproductive Confrontational Networks technique was used in an innovative way, which allows a large number of unlabelled images to be used with a semi-supervised training style. In this paper; nearly half of the retrieved images were manually annotated for the presence of seven discrete facial expressions and the intensity of valence and arousal. From facial expressions, as well as the primary theoretical frameworks that have been offered to explain these patterns, we propose that this is an area of inquiry that would benefit from an ecological approach in which contextual elements are more explicitly considered and reflected in experimental methods and may suggest heretofore unexplored underlying mechanisms.