In this paper, we propose a novel feature guided Gaussian mixture model (FG-GMM) for image matching, which typically requires matching two sets of feature points extracted from the given images. We formulate the problem as estimation of a feature guided mixture of densities: a GMM is fitted to one point set, such that both the centers and local features of the Gaussian densities are constrained to coincide with the other point set. The problem is solved under a unified maximum-likelihood framework together with an iterative semi-supervised Expectation-Maximization (EM) algorithm initialized by the confident feature correspondences. The image transformation is specified in a reproducing kernel Hilbert space and a sparse approximation is adopted to achieve a fast implementation. Extensive experiments on various real images show the robustness of our approach, which consistently outperforms other state-of-the-art methods.