Facial expressions are very a important source of information for the development of new technologies. Humans communicate our emotions through our faces, and Psychologists have studied emotions in faces since the early works of Charles Darwin . One of the most successful emotion models is the Facial Action Coding System (FACs ), where a particular set of action units (facial muscle movements) act as the building blocks of 6 basic emotions (happiness, surprise, fear, anger, disgust, sadness).
The automatic understanding of this universal language (very similar in almost all cultures) is one of the most important research areas in computer vision, with applications in many fields, such as design of intelligent user interfaces, human-computer interaction, diagnosis of disorders or even the reactive publicity field. In this research line we propose to apply and design state-of-the-art supervised algorithms to detect and classify emotions and Action Units.
Nevertheless, there exist far more emotions in addition to this basic set. We can predict with better than chance accuracy: the results of a negotiation, the preferences of the users in binary decisions , the deception perception, etc. In this research line we collaborate with the Social Perception Lab from Princeton University (http://tlab.princeton.edu/) to apply automated algorithms to real data from Pyschology Labs.
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 Darwin, Charles (1872), The expression of the emotions in man and animals, London: John Murray.
 P. Ekman and W. Friesen. Facial Action Coding System: A Technique for the Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto, 1978.
 Masip D, North MS, Todorov A, Osherson DN (2014) Automated Prediction of Preferences Using Facial Expressions. PLoS ONE 9(2): e87434. doi:10.1371/journal.pone.0087434