Face Recognition for Smart Environments

Researchers today are actively building smart environments (i.e. visual, audio, and haptic interfaces to environments such as rooms, cars, and office desks) [1,2]. In these applications a key goal is usually to give machines perceptual abilities that allow them to function naturally with people -- to recognize the people and remember their preferences and peculiarities, to know what they are looking at, and to interpret their words, gestures, and unconscious cues such as vocal prosody and body language. Researchers are using these perceptually-aware devices to explore applications in health care, entertainment, and collaborative work.

Recognition of facial expression is an important example of how face recognition interacts with other smart environment capabilities. It is important that a smart system knows whether the user looks impatient because information is being presented too slowly, or confused because it is going too fast -- facial expressions provide cues for identifying and distinguishing between these different states. In recent years much effort has been put into the area of recognizing facial expression, a capability that is critical for a variety of human-machine interfaces, with the hope of creating a person-independent expression recognition capability. While there are indeed similarities in expressions across cultures and across people, for anything but the most gross facial expressions analysis must be done relative to the person's normal facial rest state -- something that definitely isn't the same across people. Consequently, facial expression research has so far been limited to recognition of a few discrete expressions rather than addressing the entire spectrum of expression along with its subtle variations. Before one can achieve a really useful expression analysis capability one must be able to first recognize the person, and tune the parameters of the system to that specific person.

  

Figure: Wearable Face Recognition System