Illumination insensitive recognition using 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 gradient based filter bank into the eigenspace recognition framework. We show that the eigenimage coefficients are invariant to linear filtering (input and eigenimages are filtered with same filters). To achieve further illumination insensitivity we devised a robust procedure for coefficient recovery. The proposed approach has been extensively evaluated on a set of 4932 images and the results were compared to other approaches.