The same biometric capabilities that enhance security can also improve the interactions between artificial beings and humans. The first step is detecting that a face is present. One research group, led by Takeo Kanade of Carnegie Mellon University, has in the last several years found accurate ways to pick out faces from complex cluttered backgrounds, using probabilistic methods and also a neural net. As presented earlier, a neural net is a set of interconnected processors that can be trained to acquire and store knowledge—in this case, how to decide whether a given image contains a face. In the approach Kanade’s group devised, the network examines a still image in small pieces, some chosen to filter for facelike features; for instance, one piece consists of horizontal stripes 20 pixels wide by five pixels high, a configuration that tends to pick out a mouth or pair of eyes in a face presented in full frontal view.
The researchers trained the network with more than a thousand assorted images of faces, and also with images deliberately chosen not to contain faces. As we ourselves do, the network sometimes incor- rectly found faces where there were none. These erroneous choices became examples of what not to identify as a face, thereby sharpening the network’s judgment. Once trained, the system was tested on hun- dreds of new images including photographs of individuals and groups, the Mona Lisa, and the face cards from a deck of playing cards. The network found up to 90 percent of the faces, depending on the trade- off between making the identification highly certain and allowing a few incorrect identifications to slip through. The approach using probabilistic methods was even more effective, in that it also worked well for faces seen in profile and in three-quarters view. (You can try both approaches at Web sites maintained by Kanade’s group, where anyone can submit test images. Each face that the algorithms find is returned neatly surrounded by a green outline, leaving no doubt of the effectiveness of the methods.)
This kind of face detection can also be carried out in real time, a requirement for robotic applications. One example of software for real-time detection, developed by the German-based Fraunhofer In- stitute for Integrated Circuits, can be downloaded from theirWeb site. Used on images generated by an inexpensive video camera connected