为解决在刑侦领域需要通过脚印信息预测身高的问题，文中提出一种基于深度学习的回归预测算法。该算法首先对原始数据进行预处理来得到适用于深度学习回归模型的数据集，然后根据足迹数据的特性提出了一种由边缘提取和回归预测两个部分组成的新型网络架构 FtH-Net (Foot to Height-Net)，最后基于预处理得到的数据集和 FtH-Net训练得到一个性能良好的预测模型。实验结果表明，相比于传统方法，该方法在保证模型泛化能力的同时大幅度提升了预测的准确率，预测身高 2 cm 以内的准确率达到了67%。

A regression prediction algorithm based on deep learning was proposed to solve the problem of predicting height through footprint information in criminal investigation． Firstly，the original data was preprocessed to obtain the data set suitable for the deep learning regression model． Secondly，a new network architecture foot to height-net (FtH-Net) containing edge extraction and regression was proposed according to the characteristics of the footprint data． Finally，a prediction model with good performance was achieved by the data set based on the first two steps and regression network training． The experimental results show that，compared with the traditional one，the new method can greatly improve the accuracy of prediction while ensuring the generalization ability of the model，and the accuracy of prediction for people within 2cm height can reach 67%．