An Image-Text Dual-Channel Union Network for Person Re-Identification

Baoguang Qi, Yi Chen, Qiang Liu, Xiaohai He, Linbo Qing, Ray E. Sheriff, Honggang Chen*

*Corresponding author for this work

Research output: Contribution to journalArticle (journal)peer-review

5 Citations (Scopus)
208 Downloads (Pure)


Multiple people can have similar appearances and portions of images can be occluded or have viewpoint changes in real scenarios, causing the increased difficulty of person reidentification (Re-ID). To address these problems, we propose
a dual-channel person Re-ID algorithm that integrates person Re-ID image-text pairs into the classification network for end-to end learning. We construct an image channel and a text channel, and subsequently extract visual information and text information using a convolutional neural network and a simple recurrent units network, respectively. The text information is used to assist in the learning of visual information, consequently improving
the robustness of the visual information. In addition, the visual features are divided into two branches to calculate the global and local features. Global features focus on the overall appearance of a person, whereas local features provide more fine-grained detail. Text information is more accurate and reliable, and it is thus more robust to occlusion and viewpoint changes. Visual information complemented by text information can describe a person more
accurately and reliably. Extensive experiments demonstrate our method achieves state-of-the-art performance
Original languageAmerican English
Article number2513216
Pages (from-to)1-16
Number of pages16
JournalIEEE Transactions on Instrumentation and Measurement
Early online date21 Apr 2023
Publication statusPublished - 18 May 2023


  • Person re-identification
  • Text feature
  • Global and local visual features
  • Convolutional neural network
  • Recurrent units network
  • person re-identification (Re-ID)
  • text feature
  • recurrent units network
  • global and local visual features
  • Convolutional neural network (CNN)


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