Person re-identification (re-ID), which aims to identify the same individual from a gallery
collected with different cameras, has attracted increasing attention in the multimedia retrieval community.
Current deep learning methods for person re-ID focus on learning classification models on training identities to obtain an ID-discriminative embedding (IDE) extractor, which is used to extract features from testing images for re-ID. The IDE features of the testing identities might not be discriminative due to that the training identities are different from the testing identities. In this paper, we introduce a new ID-adaptation network (ID-AdaptNet), which aims to improve the discriminative power of the IDE features of the testing identities for better person re-ID. The main idea of the ID-AdaptNet is to transform the IDE features to a
common discriminative latent space, where the representations of the ‘‘seen’’ training identities are enforced
to adapt to those of the ‘‘unseen’’ training identities. More specifically, the ID-AdaptNet is trained by
simultaneously minimizing the classification cross-entropy and the discrepancy between the ‘‘seen’’ and the
‘‘unseen’’ training identities in the hidden space. To calculate the discrepancy, we represent their probability
distributions as moment sequences and calculate their distance using their central moments. We further
propose a stacking ID-AdaptNet that jointly trains multiple ID-AdaptNets with a regularization method
for better re-ID. Experiments show that the ID-AdaptNet and stacking ID-AdaptNet effectively improve the
discriminative power of IDE features.