In this paper, we propose a topology preserving graph matching (TPGM) method for partial face recognition. Most existing face recognition methods extract features from holistic face images, yet faces in real-world unconstrained environments are usually occluded by objects or other faces, which cannot provide the whole face images for recognition. Latest keypoint-based partial face recognition methods only match on the detected keypoints to remove the occluded regions. However, they simply measure the node-wise similarity without higher order geometrical graph information, thereby depending heavily on descriptors which are susceptible to noises. To address this, our TPGM method estimates a non-rigid transformation encoding the second order geometric structure of the graph, so that more accurate and robust correspondence can be computed with the topological information. Experimental results on three widely used face datasets show that the proposed TPGM outperforms most existing state-of-the-art partial face recognition methods.