Person Re-Identification by Unsupervised l1 Graph Learning

Most existing person re-identification (Re-ID) methods are based on supervised learning of a discriminative distance metric. They thus require a large amount of labelled training image pairs which severely limits their scalability. In this work, we propose a novel unsupervised Re-ID approach which requires no labelled training data yet is able to capture discriminative information for cross-view identity matching. Our model is based on a new graph regularised dictionary learning algorithm. By introducing a l(1)-norm graph Laplacian term, instead of the conventional squared l(2)-norm, our model is robust against outliers caused by dramatic changes in background, pose, and occlusion typical in a Re-ID scenario. Importantly we propose to learn jointly the graph and representation resulting in further alleviation of the effects of data outliers. Experiments on four benchmark datasets demonstrate that the proposed model significantly outperforms the state-of-the-art unsupervised learning based alternatives whilst being extremely efficient to compute.