Using image registration algorithms leads to poor results when the color of original images is simple and when time complexities are high.Thus

we propose the method fast speed up robust feature (SURF)keypoint registration algorithm by using fusion features. Color invariance margin and central symmetry-local binary pattern(CS-LBP)as fusion features are first extracted

and the variance of quantified color histogram for the weight of the fusion features is calculated. SURF keypoints and descriptors on grayscale are extracted. The use of the nearest neighbor match method requires rough matches

whereas the use of improved random sample consensus(RANSAC) algorithm requires fine matches. Least Square Method(LMS)needs transform relation to register. Experimental results show that the proposed algorithm can extract robust SURF features in fusion feature

and using these features for image registration can improve precision by 5% and decrease time complexity by 15%. Thus

this method can pervade images of all situations. The proposed algorithm can obtain enough keypoints to improve precision and robustness; the improved RANSAC decreases time complexity and iterations.