Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos

TL;DR

This paper introduces ShoeRinsics, a method for creating a comprehensive forensic shoeprint database by predicting 3D depth maps from online shoe-tread photos using synthetic data and domain adaptation techniques.

Contribution

The paper presents a novel approach to generate 3D shoeprint data from online images, addressing the lack of existing large-scale, depth-annotated shoeprint databases.

Findings

ShoeRinsics outperforms existing depth prediction methods.

Domain adaptation improves synthetic-to-real transfer.

Benchmark results validate the method's effectiveness.

Abstract

Shoe tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct shoe models. Moreover, the database is preferred to contain the 3D shape, or depth, of shoe-tread photos so as to allow for extracting shoeprints to match a query (crime-scene) print. We propose to address this gap by leveraging shoe-tread photos collected by online retailers. The core challenge is to predict depth maps for these photos. As they do not have ground-truth 3D shapes allowing for training depth predictors, we exploit synthetic data that does. We develop a method termed ShoeRinsics that learns to predict depth by leveraging a mix of fully supervised synthetic data and unsupervised retail image data. In particular, we find domain adaptation and intrinsic image decomposition techniques effectively mitigate the synthetic-real domain gap and yield significantly better depth prediction. To validate our method, we introduce 2 validation sets consisting of shoe-tread image and print pairs and define a benchmarking protocol to quantify the quality of predicted depth. On this benchmark, ShoeRinsics outperforms existing methods of depth prediction and synthetic-to-real domain adaptation.