‘If snow be white, why then her breasts are dun.’ –Shakespeare


As an advanced research topic in forensics science, automatic shoe-print identification has been extensively studied in the last two decades, since shoe marks are the clues most frequently left in a crime scene. […] A large variety of handcrafted features have been used for automatic shoe-print identification. These features have shown good performance in limited and controlled scenarios. Unfortunately, they fail when they are dealing with large intra-class variations caused by the noise, oc- clusions, rotation and various scale distortions. A good alternative to these conventional features are the learned ones, e.g. deep learning, which have more generalization ability in more complicated scenarios. To be effective, these models need to be trained on a large amount of data.

{ arXiv | PDF }