GUV-Net for high fidelity shoeprint generation

Muhammad Hassan, Yan Wang, Wei Pang, Di Wang, Daxi Li, You Zhou, Dong Xu

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Abstract

Shoeprints contain valuable information for tracing evidence in forensic scenes, and they need to be generated into cleaned, sharp, and high-fidelity images. Most of the acquired shoeprints are found with low quality and/or in distorted forms. The high-fidelity shoeprint generation is of great significance in forensic science. A wide range of deep learning models has been suggested for super-resolution, being either generalized approaches or application specific. Considering the crucial challenges in shoeprint based processing and lacking specific algorithms, we proposed a deep learning based GUV-Net model for high-fidelity shoeprint generation. GUV-Net imitates learning features from VAE, U-Net, and GAN network models with special treatment of absent ground truth shoeprints. GUV-Net encodes efficient probabilistic distributions in the latent space and decodes variants of samples together with passed key features. GUV-Net forwards the learned samples to a refinement-unit proceeded to the generation of high-fidelity output. The refinement-unit receives low-level features from the decoding module at distinct levels. Furthermore, the refinement process is made more efficient by inverse-encoded in high dimensional space through a parallel inverse encoding network. The objective functions at different levels enable the model to efficiently optimize the parameters by mapping a low quality image to a high-fidelity one by maintaining salient features which are important to forensics. Finally, the performance of the proposed model is evaluated against state-of-the-art super-resolution network models.
Original languageEnglish
Pages (from-to)933–947
Number of pages15
JournalComplex and Intelligent Systems
Volume8
Issue number2
Early online date21 Oct 2021
DOIs
Publication statusPublished - Apr 2022

Keywords

  • Forensics
  • GAN
  • Infusion
  • Shoeprint
  • Super-resolution
  • U-Net
  • VAE

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Engineering (miscellaneous)
  • Computational Mathematics

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