Are you ready for ‘mood computers’ – PCs that are so personal they can read your facial expressions and determine whether you’re happy, sad, confused or lying, and then respond accordingly?

Ready or not, such intimate interfaces are currently in the works at two major computer research centers. At the MIT Media Laboratory in Cambridge, Mass., researchers are dabbling in `perceptual intelligence’ – computers that read and respond to expressed human behavior – while a group at the Carnegie Mellon University Robotics Institute in Pittsburgh has already developed a prototype computer vision system that automatically distinguishes among subtle facial expressions.

The point? Scientists believe that if computers can be programmed to detect the subtleties of facial expressions and physical gestures, then they can be adapted to suit any number of real-world applications, including:

n Criminal investigation. Imagine a polygraph machine that could add face

analysis to its kit of lie-detection tools.

n Biomedical applications, including treatment of facial nerve disorders.

n Security systems. Facial recognition could be key in controlling access to buildings and computer systems.

“There are lots of real-world applications,” says Alex Pentland, academic head of the Media Lab at MIT. Among Pentland’s current projects are ‘Smart Rooms’ and ‘Smart Desks’, which use cameras, microphones and other sensors to recognize people, recall their preferences and anticipate their needs. “By knowing the situation, whether you’re in a meeting, driving a car, or talking to a client, information can be better tailored to the user’s needs,” Pentland says.

At Carnegie Mellon, researchers under the direction of adjunct faculty member Jeffrey Cohn have developed a computer vision system called Auto-mated Face Analysis that automatically recognizes subtly different facial expressions.

Face analysis could prove valuable to such diverse clients as product marketers and politicians, who could really see how well their messages are being received.

These systems are just in the development stage. Says Cohn, “I would see two to four years as a likely deployment time frame.” – Tom Field

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