Biometric Authentication: A Machine Learning Approach

8.5. PDBNN Face Recognition System Case Study

A PDBNN-based face recognition system was developed through a collaboration between Siemens Corporate Research, Princeton, and Princeton University [188, 208—210]. The total system diagram is depicted in Figure 8.7. All four main modules— face detector, eye localizer, feature extractor, and face recognizer—were implemented on a Sun Spare 10 workstation. An RS-170 format camera with 16mm, f1.6 lens was used to acquire image sequences. The S1V digitizer board digitized the incoming image stream into 640x480 8-bit grayscale images and stored them into the frame buffer. The image-acquisition rate was approximately 4 to 6 frames per second. The acquired images were downsized to 320x240 for the following ...