Visible–infrared cross-modality person re-identification is a realistic problem of person re-identification. Under poor illumination scenario, general methods of visible–visible person re-identification can not solve the problem well. If we directly compare the visible images of pedestrians captured under dark lighting with the visible images of pedestrians captured under normal light, this extreme color deviation will greatly reduce the recognition ability of the learned representations. In this paper, we propose a dual-path framework for visible–infrared cross-modality person re-identification based human part level features. Feature learning module contains modality-specific dual-path layers and modality-shared human part-level layers, which achieve discriminative global and local representations. In order to better optimize the proposed network, we design a global loss function and a local loss function for the global features and local features, respectively. The two loss functions are integrated together to train the network. We verify the effectiveness of our method on the challenging benchmarks: SYSU-MM01 and RegDB. Experimental results show that, compared with other cross-modality methods, our method has better effect in improving visible–infrared cross-modality person re-identification tasks.

中文翻译：

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可视化红外人重新识别的双路径部件级别方法

可见红外跨模态人员重新识别是人员重新识别的现实问题。在照明不佳的情况下，可见-可见人员重新识别的一般方法不能很好地解决该问题。如果直接将在暗光下拍摄的行人的可见图像与在正常光下拍摄的行人的可见图像直接进行比较，则这种极端的颜色偏差将大大降低所学习表示的识别能力。在本文中，我们提出了一种基于人的零件级特征的可见-红外跨模态人员重新识别的双路径框架。特征学习模块包含特定于模态的双路径层和模态共享的人类零件层，从而实现了具有区别性的全局和局部表示。为了更好地优化所提出的网络，我们分别针对全局特征和局部特征设计了全局损失函数和局部损失函数。这两个丢失功能集成在一起以训练网络。我们在具有挑战性的基准上验证了我们方法的有效性：SYSU-MM01和RegDB。实验结果表明，与其他跨模态方法相比，我们的方法在改善可见红外跨模态人员重新识别任务方面具有更好的效果。