A sample diversity and identity consistency based cross-modality model for visible-infrared person re-identification

Jia Sun; Yanfeng Li; Houjin Chen; Yahui Peng

doi:10.1117/12.2644355

12 October 2022 A sample diversity and identity consistency based cross-modality model for visible-infrared person re-identification

Jia Sun, Yanfeng Li, Houjin Chen, Yahui Peng

Author Affiliations +

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420U (2022) https://doi.org/10.1117/12.2644355
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Visible-infrared person re-identification (VI-ReID) aims to search person images across cameras of different modalities, which can address the limitation of visible-based ReID in dark environments. It is a very challenging task, as images of the same identity have huge discrepancy in different modalities. To address this problem, a cross-modality ReID model based on sample diversity and identity consistency is proposed in this paper. For sample diversity, auxiliary images are introduced based on the idea of information transfer. The auxiliary images combine the information of visible images and infrared images, and can improve the diversity of input data and robustness of the network. For identity consistency, homogeneous distance loss and heterogeneous distance loss are developed from four different perspectives to shorten the distance between the samples of same identities. Extensive experimental results demonstrate the effectiveness of the proposed method.

Citation Download Citation

Jia Sun, Yanfeng Li, Houjin Chen, and Yahui Peng "A sample diversity and identity consistency based cross-modality model for visible-infrared person re-identification", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420U (12 October 2022); https://doi.org/10.1117/12.2644355

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