Robust Recognition against Illumination Variations Based on SIFT

Feature matching is one of the basic approaches to many of computer vision applications, such as object recognition. Dealing with illumination variations is an open problem in this field. In this paper we present an approach to make a more robust algorithm against real world illumination changes and variations in direction of the light source on our object of interest, by using a set of training images for sampling these variations from their SIFT keypoints. A comprehensive keypoint descriptor based on the variations of illumination in training data is acquired to have a high recognition rate against real 3D illumination changes. This large number of keypoints is simplified to achieve a smaller number of robust keypoints and significantly faster matching phase.