Person re-identification by order-induced metric fusion

This paper presents a novel two-pronged framework for person re-identification. Its idea articulates over the fact that distinct descriptors manifest different ranking scores for the same probe pattern. Thus, if conveniently fused, the descriptors in hand are ought to compensate each other, leading to significant improvements. In this respect, this paper proposes a learning-free weighting method that penalizes and averages the re-identification estimates (e.g., distances) pointed out by different descriptors according to their confidence in evidencing the correct match, to a given probe person, among a given gallery. We particularly show that tangible improvements can be attained with respect to utilizing each descriptor individually. Moreover, we consider a confidence measure mechanism that treats the mutual pairwise distances within the gallery, in order to raise the scores obtained at the fusion stage, and we show that interesting improvements can be achieved. We evaluate the proposed framework on four benchmark datasets and advance late works by large margins.