Recently

most local invariant features for image matching problems center around the invariance of rigid deformation.However

there is a widespread and growing need of the image matching for the non-rigid deformations.The complexity of non-rigid deformation makes the corresponding points' local support area difficult to maintain the structure similarity.So how to build the more pertinent local invariant feature descriptor is the key to solving non-rigid deformation image matching problems.We

therefore

propose an oriented non-rigid deformation local invariant feature descriptor and its structuring method to better specialize the direction of the local feature and divide local support area.We first estimate the elliptical neighborhood of each feature point and indicate the main direction according to its second moment matrix

and then normalized the local support area to eliminate differences in the anisotropic deformation.Finally

we embed the local area into 3D space and construct a descriptor in terms of a heat kernel signature

which is invariant to deformation.Comparative experiments show that

under the non-rigid deformation and photometric changes

our algorithm maintains good rotation invariance and a higher matching accuracy compared with the SIFT(Scale Invariant Feature Transform) and GIH(Geodesic Intensity Histogram) algorithm.