Discriminative Image Descriptors for Person Re-identification

This chapter looks at person re-identification from a computer vision point of view, by proposing two new image descriptors designed for matching people bounding boxes in images. Indeed, one key issue of person re-identification is the ability to measure the similarity between two person-centered image regions, allowing to predict if these regions represent the same person despite changes in illumination, viewpoint, background clutter, occlusion, and image quality/resolution. They hence heavily rely on the signatures or descriptors used for representing and comparing the regions. The first proposed descriptor is a combination of Biologically Inspired Features (BIF) and covariance descriptors, while the second builds on the recent advances of Fisher Vectors. These two image descriptors are validated through experiments on two different person re-identification benchmarks (VIPeR and ETHZ), achieving state-of-the-art performance on both datasets.