The strategy has worked well. The system then models the face in terms of what similarities it bears to the eigenfaces. Next, it compares the model with those of known people. If any of the similarity scores are close, the system assumes that it has identified the user. Using this method, our smart rooms have accurately recognized individual faces 99 percent of the time amid groups of several hundred.
Facial expression is almost as important as identity.
A teaching program, for example, should know if its students look bored. So once our smart room has found and identified someone's face, it analyzes the expression. Yet another computer compares the facial motion the camera records with maps depicting the facial motions involved in making various expressions. Each expression, in fact, involves a unique collection of muscle movements. Recognizing a person's face, expression and speech is just the first step.