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Person re-identification refers to recognizing people across non-overlapping cameras at different times and locations. Due to the variations in pose, illumination condition, background, and occlusion, person re-identification is inherently difficult. In this paper, we propose a reference-based method for across camera person re-identification. In the training, we learn a subspace in which the correlations of the reference data from different cameras are maximized using Regularized Canonical Correlation Analysis (RCCA). For re-identification, the gallery data and the probe data are projected into the RCCA subspace and the reference descriptors (RDs) of the gallery and probe are constructed by measuring the similarity between them and the reference data. The identity of the probe is determined by comparing the RD of the probe and the RDs of the gallery. Experiments on benchmark dataset show that the proposed method outperforms the state-of-the-art approaches.