The translation of shoeprint to barefoot footprint based on SM-GAN

Footprints are important information at the crime scene and play an important role in the field of criminal investigation. At present, the research on footprints mainly focuses on barefoot footprints, but the main thing obtained at the crime scene is shoeprints. How to mine barefoot footprints through shoeprints is one point of the key problems in the field of footprint recognition. This paper takes optical footprints as the research object, collects 95 people’s cloth shoeprints and barefoot footprints, and proposes a generative adversarial network which combines self-attention modules and multiscale discriminator (SM-GAN). The self-attention module is added to the generator, which enables the network to focus on the association between footprint structures. The discriminator uses a multiscale discriminator structure, which improves the generation effect of the generated image in the global and local areas. The experimental results show that the method proposed in this paper has a better effect of generating footprints than traditional image-to-image translation methods.