Facial expressions are a very important source of information for the development of new technologies. As humans we use our faces to communicate our emotions, and psychologists have studied emotions in faces since the publication of Charles Darwin’s early works 1. One of the most successful emotion models is the Facial Action Coding System (FACS) 2, where a particular set of action units (facial muscle movements) act as the building blocks of six 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. It has applications in many fields, such as design of intelligent user interfaces, human-computer interaction, diagnosis of disorders and even in the field of reactive publicity. In this line of research we propose to design and apply state-of-the-art supervised algorithms to detect and classify emotions and Action Units.
Nevertheless, there is a far greater range of emotions than just this basic set. We can predict with better than chance accuracy: the results of a negotiation, the preferences of the users in binary decisions 3, the deception perception, etc. In this line of research we collaborate with the Social Perception Lab at Princeton University (http://tlab.princeton.edu/) to apply automated algorithms to real data from psychology labs.
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