Visible–infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges that are common for both ReId and VI-ReId such as pose/illumination variations, background clutter and occlusion, VI-ReId has additional challenges as color information is not available in infrared images. As a result, the performance of VI-ReId systems is typically lower than that of ReId systems. In this work, we propose a four-stream framework to improve VI-ReId performance. We train a separate deep convolutional neural network in each stream using different representations of input images. We expect that different and complementary features can be learned from each stream. In our framework, grayscale and infrared input images are used to train the ResNet in the first stream. In the second stream, RGB and three-channel infrared images (created by repeating the infrared channel) are used. In the remaining two streams, we use local pattern maps as input images. These maps are generated utilizing local Zernike moments transformation. Local pattern maps are obtained from grayscale and infrared images in the third stream and from RGB and three-channel infrared images in the last stream. We improve the performance of the proposed framework by employing a re-ranking algorithm for post-processing. Our results indicate that the proposed framework outperforms current state-of-the-art with a large margin by improving Rank-1/mAP by $29.79\text{\%}∕30.91\text{\%}$ on SYSU-MM01 dataset, and by $9.73\text{\%}∕16.36\text{\%}$ on RegDB dataset.

可见-红外跨模态人员重新识别（VI-ReId）是在光线较弱或黑暗的环境中进行视频监视的一项重要任务。尽管最近有许多关于在可见域（ReId）中对人进行重新识别的研究，但很少有研究专门针对VI-ReId。除了ReId和VI-ReId常见的挑战（例如姿势/照明变化，背景杂波和遮挡）外，VI-ReId还有其他挑战，因为红外图像中没有颜色信息。结果，VI-ReId系统的性能通常低于ReId系统。在这项工作中，我们提出了一个四流框架来提高VI-ReId性能。我们使用输入图像的不同表示形式在每个流中训练一个单独的深度卷积神经网络。我们希望可以从每个流中学习不同且互补的功能。在我们的框架中，使用灰度和红外输入图像在第一流中训练ResNet。在第二个流中，使用RGB和三通道红外图像（通过重复红外通道创建）。在其余的两个流中，我们使用局部模式图作为输入图像。这些地图是利用本地Zernike矩变换生成的。从第三流中的灰度和红外图像以及最后流中的RGB和三通道红外图像中获取局部图案图。我们通过采用重新排序算法进行后处理来提高所提出框架的性能。我们的结果表明，通过改善Rank-1 / mAP，建议的框架以较大的幅度胜过当前的最新技术。$29。79\text{％}∕30。91\text{％}$ 在SYSU-MM01数据集上 $9。73\text{％}∕16。36\text{％}$ 在RegDB数据集上。