Resting-state fMRI has proven to entail subject-specific signatures that can serve as a fingerprint to identify individuals. Conventional methods are based on building a connectivity matrix based on correlation between the average time course of pairs of brain regions. This approach, first, disregards the exquisite spatial detail manifested by fMRI due to working on average regional activities, second, cannot disentangle correlations associated to cognitive activity and underlying noise, and third, does not account for cortical morphology that spatially constraints function. Here I propose a method to address these shortcomings via leveraging principles from graph signal processing. High spatial resolution cortical graphs that encode each individual's cortical morphology are built and region-specific, whole-hemisphere fMRI maps are used as signals that reside on the graphs.