Joint transfer component analysis and metric learning for person re-identification

Yunzhou Zhang, Yixiu Liu, Sonya Coleman, Jianning Chi

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
35 Downloads (Pure)


A novel and efficient metric learning strategy for person re-identification is proposed. Person re-identification is formulated as a multi-domain learning problem. The assumption that the feature distributions from different camera views are the same is overthrown in this Letter. ID-based transfer component analysis (IDB-TCA) is proposed to learn a shared subspace, in which the differences in the feature distribution between source domain and target domain are significantly reduced. Experimental evaluation on the CUHK01 dataset demonstrates that metric learning with IDB-TCA embedded outperforms state-of-art metric methods for person re-identification.
Original languageEnglish
Pages (from-to)821-823
JournalElectronics Letters
Issue number13
Publication statusPublished (in print/issue) - 21 Jun 2018


  • component analysis
  • person re-identification
  • machine learning


Dive into the research topics of 'Joint transfer component analysis and metric learning for person re-identification'. Together they form a unique fingerprint.

Cite this