Feature matching is a fundamental problem in computer vision. There are geometrical deformations

named plane homography

between corresponding feature windows on different images of the same patch on scene surface. But most of the state-of-the-art matching schemes approximate the homography with an affine transformation model

namely affine invariant features. The linear character of affine model not only reduces the complication of matching process

but ensures a good and stable convergence of the unmanageable iteration. However

there are no quantificational discussions about this approximation by far. In this paper

an overview of invariant features under different geometric groups was given first

and then a quantificational analysis of affine invariant features was proposed. The analytic expression of location error was deduced by our novel method

named normalization of conic equations. Experimental results of real images demonstrate the correctness and necessity of our method. Finally

some valuable conclusions and suggestions based on the above analysis are deduced both on theory and in practice.