In this white paper we propose a new method which exploits tools from graph signal processing to solve the graph matching problem, the problem of estimating the correspondence between the vertex sets of two graphs. We recast the graph matching problem as matching multiple similarity matrices where the similarities are computed between filtered signals unique to eachnode. Using appropriate graph filters, these similarity matrices can emphasize long or short range behavior and the method will implicitly search for similarities between the graphs and at multiple scales. Our method shows substantial improvementsover standard methods which use the raw adjacency matrices, especially in low-information environments.