Illumination invariant encrypted face recognition using correlation filter

One of the challenging tasks in face recognition is illumination variation. In this paper, we propose an encoding domain correlation technique for face recognition to handle the illumination problem. This technique is based on sparse representation of optical encryption of training images to form unconstrained minimum average correlation energy (UMACE) filter. Averages authentication rates for YaleB and PIE database are increased rather than other methods because illumination variations in the plain face images, do not affect encrypted images with uniform histograms. Another advantage for the proposed method is its high security. Because encrypted version of facial images are used rather than plain images at the receiver.