Deep learning model for human-intuitive shoeprint reconstruction.

Shoeprint reconstruction is essential in forensic science, but it is also challenging due to various inconsistencies in the patterns, textures, sizes, abrasions, etc. of shoeprints. The computational reconstruction of sharper and more complete shoeprints is conventionally conducted using handcrafted features, and it often requires human intervention. Prior studies using end-to-end machine learning approaches are limited in number and have not achieved a high level of performance. In this paper, we propose a model named ShoeRec, which employs variational autoencoder (VAE) as a component in a U-Net-like architecture to reconstruct missing regions and borders in shoeprint images. ShoeRec incorporates skip connections to preserve key patterns and employs VAE in the bottleneck to facilitate the reconstruction of desired shoeprints with the restoration of detail as perceived by humans. As a U-Net, the model skips the contextual information from the encoder to the decoder, and the compressed features in the latent space via VAE optimize the probabilistic distribution for reconstructing complete shoeprints. The reconstruction operation is automatically tuned according to the objective function, so as to reduce the structural correlation between the original and projected shoeprint and restore the absent information in the desired shoeprints. To the best of our knowledge, ShoeRec is the first deep learning infusion model that specializes in shoeprint reconstruction. The shoeprints reconstructed by ShoeRec have a close match with the originals in terms of both structures and patterns, and ShoeRec outperforms state-of-the-art generative models in human evaluation. • An end-to-end deep learning model focuses on shoeprint reconstruction for forensics. • Reconstruction of existing patterns together with the generation of missing regions. • Missing regions reconstruction is specifically designed based on the loss function. • The proposed model performs well for noisy, blurry, and partial input images. • Model outperforms state-of-the-art generative models in terms of human evaluation. [ABSTRACT FROM AUTHOR]