Persons captured in real-life scenarios are generally in non-uniform scales. However, most generally acknowledged person re-identification (Re-ID) methods lay emphasis on matching normal-scale high-resolution person images. To address this problem, the ideas of existing image reconstruction techniques are incorporated which are expected contribute to recover accurate appearance information for low-resolution person Re-ID. In specific, this paper proposes a joint deep learning approach for Scale-Adaptive person Super-Resolution and Re-identification (SASR<sup>2</sup> ). It is for the first time that scale-adaptive learning is jointly implemented for super-resolution and re-identification without any extra post-processing process. With the super-resolution module, the high-resolution appearance information can be automatically reconstructed from scales of low-resolution person images, bringing a direct beneficial impact on the subsequent Re-ID thanks to the joint learning nature of the proposed approach. It deserves noting that SASR<sup>2</sup> is not only simple but also flexible, since it can be adaptable to person Re-ID on both multi-scale LR and normal-scale HR datasets. A large amount of experimental analysis demonstrates that SASR<sup>2</sup> achieves competitive performance compared with previous low-resolution Re-ID methods especially on the realistic CAVIAR dataset.