足迹作为人体生物特征之一，在生物识别领域具有重要意义，而同一对象的不同鞋型压力足迹图像在足迹轮廓特征上具有显著性差异，导致其类内差异大。针对压力足迹图像的跨域检索，文中提出了一种基于互信息解耦表示的跨域压力足迹图像检索方法。首先，构建了一个包含200人足迹图像的多域压力足迹数据集，从定性和定量两个角度分析跨域压力足迹图像的特点；其次，采用两个独立的编码器实现图像解耦模块，该模块将压力足迹图像解耦为域特定表示和域共享表示，通过域分类法保证域特定表示包含更多域相关的信息；然后，通过最小化互信息损失扩大域特定表示和域共享表示之间的距离，同时，为避免解耦过程中信息的丢失，基于域特定表示和域共享表示重构原始压力足迹图像；最后，通过特征提取模块进一步提取域共享表示的深层卷积特征，经过度量模块计算不同特征间的关联度，从而实现跨域压力足迹图像检索。对比及消融实验结果表明，该方法的解耦模块具有一定的有效性，在多域压力足迹数据集上的性能表现良好，首位查询结果的检索准确率达到79.83%，平均准确率达到65.48%。

As one of human biometric features, footprint is of great significance in the field of biometric identification. However, the pressure footprint images of different shoe types for the same person have significant differences in the footprint contour features, leading to large intra-class differences. For cross-domain retrieval of pressure footprint images, this paper proposed a cross-domain pressure footprint images retrieval method based on mutual information disentangled representations. Firstly, a multi-domain pressure footprint dataset containing 200 people’s footprint images was constructed and the characteristics of cross-domain pressure footprint images were analyzed from qualitative and quantitative perspectives. Secondly, two independent encoders were used to construct an image disentanglement module, which disentangles the pressure footprint images into a domain-specific representation and a domain-shared representation, and ensures that the domain-specific representation contains more domain-related information through domain classification. Then, the distance between the domain-specific representation and the domain-shared representation was enlarged by minimizing mutual information loss. At the same time, in order to avoid the loss of information in the disentangled process, the original pressure footprint image was reconstructed based on the domain-specific representation and the domain-shared representation. Finally, the deep convolution features of the domain-shared representation were further extracted by feature extraction module and the cross-domain pressure footprint images retrieval was realized through the metric module which calculates the correlation degree between different features. The results of comparison and ablation experiments show that the disentanglement module of this method is effective and performs well on multi-domain pressure footprint dataset. The retrieval accuracy of the first query result reached 79.83%, and the average accuracy reached 65.48%.