From Re-identification to Identity Inference: Labeling Consistency by Local Similarity Constraints

In this chapter we introduce the problem of identity inference as a generalization of person re-identification. It is most appropriate to distinguish identity inference from re-identification in situations where a large number of observations must be identified without knowing a priori that groups of test images represent the same individual. The standard single- and multi-shot person re-identification common in the literature are special cases of our formulation. We present an approach to solving identity inference by modeling it as a labeling problem in a Conditional Random Field (CRF). The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space. Experimental results are given on the ETHZ, i-LIDS and CAVIAR datasets. Our approach yields state-of-the-art performance for multi-shot reidentification, and our results on the more general identity inference problem demonstrate that we are able to infer the identity of very many examples even with very few labelled images in the gallery