Locality based discriminative measure for multiple-shot human re-identification

Multiple-shot human re-identification is an important issue in both academia and industry. It addresses the problem of building correspondences among object instances appearing in a camera network using biometric cues. Among all possible cues, face is a typical one that has long been investigated, while the whole body has become a recent trend. This problem is challenging because of small intra-class similarities and inter-class dissimilarities caused by the variations of human appearance in real scenarios. Existing methods mainly involve designing a representation and/or devising a measure to explore the within-class compactness and between-class separation. Although encouraging progress has been made, the results are still far from satisfactory. In this paper, we propose a novel set-based matching model, “Locality Based Discriminative Measure”, to re-identify the human body when a set of test samples for each person are available. A new set-to-set dissimilarity is crafted considering both majorities and minorities of samples from each pair of sets. The discriminability of this dissimilarity is then further exploited by the local metric field; it can thereby serve as a more capable low-level measure to support the high-level measure for the final matching. Extensive experiments on widely used benchmarks demonstrate that our proposal remarkably outperforms state-of-the-arts.