Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (4): 1-9.doi: 10.6040/j.issn.1672-3961.0.2025.218

• Machine Learning & Data Mining •    

Person re-identification for cross-resolution identity matching

Liu Zhigang1,2, Feng Hanlin1, Zhou Yuanhe1, Su Jianheng1, Zhang Yan1,2   

  1. Liu Zhigang1, 2, Feng Hanlin1, Zhou Yuanhe1, Su Jianheng1, Zhang Yan1, 2(1. School of Computer and Information Technology, Northeast Petroleum University, Daqing 163318, Heilongjiang, China;
    2. Heilongjiang Petroleum Big Data and Intelligent Analysis Key Laboratory, Northeast Petroleum University, Daqing 163318, Heilongjiang, China
  • Published:2026-08-12

Abstract: Aiming at the problem of resolution mismatch in person recognition caused by variations in camera imaging conditions and environmental factors in real-world scenarios, a person re-identification network integrating wavelet-based super-resolution and dual-domain feature fusion was proposed. Wavelet decomposition was introduced to separate image features into structural and detail components, and wavelet convolution was employed to enlarge the receptive field and strengthen structural feature extraction. In the wavelet domain, differentiated loss functions were designed for high- and low-frequency subbands to guide the reconstruction of more discriminative super-resolution images. An attention mechanism was used to enhance the structural features of super-resolution images, and the enhanced features were gate-fused with structural features in the wavelet domain to construct a joint representation containing richer structural cues. Simulation results showed that the proposed method outperformed the mainstream methods in recognition performance. On the challenging CAVIAR dataset, the probability of the first correct matching of person images reached 67.8%, which effectively improved person re-identification under cross-resolution conditions.

Key words: person re-identification, cross-resolution, super-resolution reconstruction, wavelet convolution, attention mechanism

CLC Number: 

  • TP391
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