SKEPRID: Pose and illumination change-resistant skeleton-based person re-identification

Tuo Yu, Haiming Jin, Wai Tian Tan, Klara Nahrstedt

Research output: Contribution to journalArticlepeer-review


Currently, the surveillance camera-based person re-identification is still challenging because of diverse factors such as people's changing poses and various illumination. The various poses make it hard to conduct feature matching across images, and the illumination changes make color-based features unreliable. In this article, we present SKEPRID, 1 a skeleton-based person re-identification method that handles strong pose and illumination changes jointly. To reduce the impacts of pose changes on re-identification, we estimate the joints' positions of a person based on the deep learning technique and thus make it possible to extract features on specific body parts with high accuracy. Based on the skeleton information, we design a set of local color comparison-based cloth-type features, which are resistant to various lighting conditions. Moreover, to better evaluate SKEPRID, we build the PO&LI 2 dataset, which has large pose and illumination diversity. Our experimental results show that SKEPRID outperforms state-of-the-art approaches in the case of strong pose and illumination variation.

Original languageEnglish (US)
Article number82
JournalACM Transactions on Multimedia Computing, Communications and Applications
Issue number4
StatePublished - Nov 2018


  • Cloth-type features
  • Person re-identification
  • Pose recognition

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Networks and Communications


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