Sparse representation matching for person re-identification

The need for recognizing people across distributed surveillance cameras leads to the growth of recent research interest in person re-identification. Person re-identification aims at matching people in non-overlapping cameras at different time and locations. It is a difficult pattern matching task due to significant appearance variations in pose, illumination, or occlusion in different camera views. To address this multi-view matching problem, we first learn a subspace using canonical correlation analysis (CCA) in which the goal is to maximize the correlation between data from different cameras but corresponding to the same people. Given a probe from one camera view, we represent it using a sparse representation from a jointly learned coupled dictionary in the CCA subspace. The ℓ1 induced sparse representation are regularized by an ℓ2 regularization term. The introduction of ℓ2 regularization allows learning a sparse representation while maintaining the stability of the sparse coefficients. To compute the matching scores between probe and gallery, their ℓ2 regularized sparse representations are matched using a modified cosine similarity measure. Experimental results with extensive comparisons on challenging datasets demonstrate that the proposed method outperforms the state-of-the-art methods and using ℓ2 regularized sparse representation (ℓ1 + ℓ2) is more accurate compared to use a single ℓ1 or ℓ2 regularization term.