Automatic Facial Expression Recognition

The face is innervated by two di↵erent brain systems that compete for control of its muscles: a cortical brain system related to voluntary and controllable behavior, and a sub-cortical system responsible for involuntary expressions. The interplay between these two systems generates a wealth of information that humans constantly use to read the emotions, intentions, and interests [25] of others. Given the critical role that facial expressions play in our daily life, technologies that can interpret and respond to facial expressions automatically are likely to find a wide range of applications. For example, in pharmacology, the e↵ect of new anti-depression drugs could be assessed more accurately based on daily records of the patients’ facial expressions than asking the patients to fill out a questionnaire, as it is currently done [7]. Facial expression recognition may enable a new generation of teaching systems to adapt to the expression of their students in the way good teachers do [61]. Expression recognition could be used to assess the fatigue of drivers and air-pilots [58, 59]. Daily-life robots with automatic expression recognition will be able to assess the states and intentions of humans and respond accordingly [41]. Smart phones with expression analysis may help people to prepare for important meetings and job interviews. Thanks to the introduction of machine learning methods, recent years have seen great progress in the field of automatic facial expression recognition. Commercial real-time expression recognition systems are starting to be used in consumer applications, e.g., smile detectors embedded in digital cameras [62]. Nonetheless, considerable progress has yet to be made: Methods for face detection and tracking (the first step of automated face analysis)