In this article we introduce the problem of identity inference as a generalization of the re-identification problem. Identity inference is applicable in situations where a large number of unknown persons must be identified without knowing a priori that groups of test images represent the same individual. Standard single- and multi-shot person re-identification are special cases of our formulation. We present an approach to solving identity inference problems using a Conditional Random Field (CRF) to model identity inference as a labeling problem in the CRF. The CRF model ensures that the final labeling gives similar labels to detections that are similar in feature space, and is flexible enough to incorporate constraints in the temporal and spatial domains. Experimental results are given on the ETHZ dataset. Our approach yields state-of-the-art performance for the multi-shot re-identification task and promising results for more general identity inference problems.
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