Illumination invariant face recognition and impostor rejection using different MINACE filter algorithms
Rohit Patnaik, David P. Casasent · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
A face recognition system that functions in the presence of illumination variations is presented. It is based on the minimum noise and correlation energy (MINACE) filter. A separate MINACE filter is synthesized for each person using an automated filter-synthesis algorithm that uses a training set of illumination differences of that person and a validation set of a few faces of other persons to select the MINACE filter parameter c. The MINACE filter for each person is a combination of training images of only that person; no false-class training is done. Different formulations of the MINACE filter and the use of two different correlation plane metrics: correlation peak value and peak-to-correlation plane energy ratio (PCER), are examined. Performance results for face verification and identification are presented using images from the CMU Pose, Illumination, and Expression (PIE) database. All training and test set images are registered to remove tilt bias and scale variations. To evaluate the face verification and identification systems, a set of impostor images (non-database faces) is used to obtain false alarm scores (PFA).