Journal article

Identity Adaptation for Person Re-Identification

Q Ke, M Bennamoun, H Rahmani, S An, F Sohel, F Boussaid

IEEE Access | Institute of Electrical and Electronics Engineers | Published : 2018


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..

View full abstract


Awarded by Australian Research Council

Funding Acknowledgements

This work was supported by the Australian Research Council under Grant DP150100294, Grant DP150104251, and Grant DE120102960.