目前行人重识别的研究只关注了可见光下跨摄像头提取图像不变的特征表示，忽视了红外条件下的成像特点，并结合两种模态的研究成果很少。此外，当前行人重识别在判别两个图像时，通常是计算单个卷积层特征图的相似性，这会导致弱特征学习现象。为了解决上述问题，本文提出了基于特征金字塔的随机融合网络，它可以同时计算多个特征层级的相似性，匹配图像时是基于多个语义层的判别因子。该模型关注到红外图像的特性，并且缩小了可见光和红外模态内部负作用的偏差，平衡了模态间的异质差距，综合了局部特征和全局特征学习的优势，有效地解决了跨模态行人重识别问题。实验在SYSU-MM01数据集上对平均精确度和收敛速度进行验证。结果表明，所提的模型优于现有的先进算法，特征金字塔随机融合网络实现了快速收敛且平均精确度达到了32.12%。

Existing works in person re-identification only considers extracting invariant feature representations from cross-view visible cameras, which ignores the imaging feature in infrared domain, such that there are few studies on visible-infrared relevant modality. Besides, most works distinguish two-views by often computing the similarity in feature maps from one single convolutional layer, which causes a weak performance of learning features. To handle the above problems, we design a feature pyramid random fusion network (FPRnet) that learns discriminative multiple semantic features by computing the similarities between multi-level convolutions when matching the person. FPRnet not only reduces the negative effect of bias in intra-modality, but also balances the heterogeneity gap between inter-modality, which focuses on an infrared image with very different visual properties. Meanwhile, our work integrates the advantages of learning local and global feature, which effectively solves the problems of visible-infrared person re-identification. Extensive experiments on the public SYSU-MM01 dataset from aspects of mAP and convergence speed, demonstrate the superiorities in our approach to the state-of-the-art methods. Furthermore, FPRnet also achieves competitive results with 32.12% mAP recognition rate and much faster convergence.