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Graph regularized low-rank matrix recovery for robust person re-identification

Robust person re-identification (PRID) refers to the problem of matching individuals across non-overlapping camera views, while the images captured by either camera might be occluded or even missing. To address this challenging task, we propose a low-rank matrix recovery (LR) based approach in this paper. In addition to observing the global structure of cross-camera images via LR, we further exploit their local geometrical information via graph regularization, which preserves the recovered images with recognition guarantees. Our experiments verify the effectiveness and robustness of our approach, which is shown to perform favorably against state-of-the-art PRID methods.