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 we propose a method to address these shortcomings via leveraging principles from graph signal processing. We build high spatial resolution cortical graphs that encode each individual's cortical morphology and treat region-specific, whole-hemisphere fMRI maps as signals that reside on the graphs. fMRI graph signals are then decomposed using systems of graph spectral kernels to extract structure-informed functional signatures, which are in turn used for fingerprinting. Results on 100 subjects showed the overall superior subject differentiation power of the proposed signatures over the conventional method. Moreover, placement of the signatures within canonical functional brain networks revealed the greater contribution of high-level cognitive networks in subject identification.