Boosting Fisher vector based scoring functions for person re-identification

• We propose BFiVe, a new supervised algorithm for single-shot person re-identification.
• The descriptors are a set of compressed local Fisher vectors extracted from a coarse to fine image subdivision.
• In the training step each region gives rise to a learnt weak ranking function.
• The ranking function of the image gallery is obtained by a boosted selection of a weak learner subset.
• The matching rate at rank 1 on VIPeR is 38.9%, on 3DPes 41.7%, on PRID-2011 19.6%, and on i-LIDS-119 48.1%.

In recent years, much effort has been put into the development of novel algorithms to solve the person re-identification problem. The goal is to match a given person's image against a gallery of people. In this paper, we propose a single-shot supervised method to compute a scoring function that, when applied to a pair of images, provides a score expressing the likelihood that they depict the same individual. The method is characterized by: (i) the usage of a set of local image descriptors based on Fisher vectors, (ii) the training of a pool of scoring functions based on the local descriptors, and (iii) the construction of a strong scoring function by means of an adaptive boosting procedure. The method has been tested on four data-sets and results have been compared with state-of-the-art methods clearly showing superior performance.

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