Exploring modality-shared appearance features and modality-invariant relation features for cross-modality person Re-IDentification

Nianchang Huang, Jianan Liu, Yongjiang Luo, Qiang Zhang*, Jungong Han

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

Most existing cross-modality person Re-IDentification works rely on discriminative modality-shared features for reducing cross-modality variations and intra-modality variations. Despite their preliminary success, such modality-shared appearance features cannot capture enough modality-invariant discriminative information due to a massive discrepancy between RGB and IR images. To address this issue, on top of appearance features, we further capture the modality-invariant relations among different person parts (referred to as modality-invariant relation features), which help to identify persons with similar appearances but different body shapes. To this end, a Multi-level Two-streamed Modality-shared Feature Extraction (MTMFE) sub-network is designed, where the modality-shared appearance features and modality-invariant relation features are first extracted in a shared 2D feature space and a shared 3D feature space, respectively. The two features are then fused into the final modality-shared features such that both cross-modality variations and intra-modality variations can be reduced. Besides, a novel cross-modality center alignment loss is proposed to further reduce the cross-modality variations. Experimental results on several benchmark datasets demonstrate that our proposed method exceeds state-of-the-art algorithms by a wide margin.

Original languageEnglish
Article number109145
JournalPattern Recognition
Volume135
Early online date10 Nov 2022
DOIs
Publication statusPublished - 01 Mar 2023

Keywords

  • Cross-modality person Re-IDentification
  • Modality-invariant relation features
  • Modality-shared appearance features
  • Thermal infrared images
  • Visible images

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