Cross-view Discriminative Feature Learning for Person Re-Identification

Alessandro Borgia, Yang Hua, Elyor Kodirov, Neil Robertson

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
551 Downloads (Pure)


The viewpoint variability across a network of nonoverlapping cameras is a challenging problem affecting person re-identification performance. In this paper, we investigate how to mitigate the cross-view ambiguity by learning highly discriminative deep features under the supervision of a novel loss function. The proposed objective is made up of two terms, the Steering Meta Center (SMC) term and the Enhancing Centres Dispersion (ECD) term that steer the training process to mining effective intra-class and inter-class relationships in the feature domain of the identities. The effect of our loss supervision is to generate a more expanded feature space of compact classes where the overall level of inter-identities interference is reduced. Compared to the existing metric learning techniques, this approach has the advantage of achieving a better optimization because it jointly learns the embedding and the metric contextually. Our technique, by dismissing side-sources of performance gain, proves to enhance the CNN invariance to viewpoint without incurring increased training complexity (like in Siamese or Triplet networks) and outperforms many related state-of-the-art techniques on Market- 1501 and CUHK03.
Original languageEnglish
Pages (from-to)5338 - 5349
JournalIEEE Trans. on Image Processing
Issue number11
Early online date27 Jun 2018
Publication statusPublished - Nov 2018


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