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 facial images. However, faces in real-world unconstrained environments may be occluded by objects or other faces, which cannot provide the whole face images for description. Keypoint-based partial face recognition methods such as multi-keypoint descriptor with Gabor ternary pattern and robust point set matching match the local keypoints for partial face recognition. However, they simply measure the nodewise similarity without higher order geometric graph information, 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. In order to exploit higher order topological information, we propose a topology preserving structural matching method to construct a higher order structure for each face and estimate the transformation. Experimental results on four widely used face data sets demonstrate that our method outperforms most existing state-of-the-art face recognition methods.