Dictionary Learning with Iterative Laplacian Regularisation for Unsupervised Person Re-identification.

Many existing approaches to person re-identification (Re-ID) are based on supervised learning, which requires hundreds of matching pairs to be labelled for each pair of cameras. This severely limits their scalability for real-world applications. This work aims to overcome this limitation by developing a novel unsupervised Re-ID approach. The approach is based on a new dictionary learning for sparse coding formulation with a graph Laplacian regularisation term whose value is set iteratively. As an unsupervised model, the dictionary learning model is well-suited to the unsupervised task, whilst the regularisation term enables the exploitation of cross-view identity-discriminative information ignored by existing unsupervised Re-ID methods. Importantly this model is also flexible in utilising any labelled data if available. Experiments on two benchmark datasets demonstrate that the proposed approach significantly outperforms the state-of-the-arts.