Pose-invariant face-head identification using a bank of neural networks and the 3D neck reference point
Michael Hild, Kazunobu YOSHIDA, Motonobu HASHIMOTO · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2003
A method for recognizing faces in relativley unconstrained environments, such as offices, is described. It can recognize faces occurring over an extended range of orientations and distances relative to the camera. As the pattern recognition mechanism, a bank of small neural networks of the multilayer perceptron type is used, where each perceptron has the task of recognizing only a single person's face. The perceptrons are trained with a set of nine face images representing the nine main facial orientations of the person to be identified, and a set face images from various other persons. The center of the neck is determined as the reference point for face position unification. Geometric normalization and reference point determination utilizes 3-D data point measurements obtained with a stereo camera. The system achieves a recognition rate of about 95%.