We propose a novel appearance-based method for person re-identification, that condenses a set of frames of an individual into a highly informative signature, called the Histogram Plus Epitome, HPE. It incorporates complementary global and local statistical descriptions of the human appearance, focusing on the overall chromatic content via histogram representation, and on the presence of recurrent local patches via epitomic analysis. The re-identification performance of HPE is then augmented by applying it as human part descriptor, defining a structured feature called asymmetry-based HPE (AHPE). The matching between (A)HPEs provides optimal performances against low resolution, occlusions, pose and illumination variations, defining state-of-the-art results on all the considered datasets.