Illumination insensitive eigenspaces

Variations in illumination can have a dramatic effect on the appearance of an object in an image. In this paper we propose how to deal with illumination variations in eigenspace methods. We demonstrate that the eigenimages obtained by a training set under a single illumination condition (ambient light) can be used for recognition of objects taken under different illumination conditions. The major idea is to incorporate a set of gradient based filter banks into the eigenspace recognition framework. This can be achieved since the eigenimage coefficients are invariant for linearly filtered images (input and eigenimages). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 2160 images and the results were compared to other approaches.