Adapting Convolutional Neural Networks on the Shoeprint Retrieval for Forensic Use

Shoeprint is an important evidence for crime investigation. Many automatic shoeprint retrieval methods have been proposed in order to efficiently provide useful information for the identification of the criminals. In the mean time, the convolutional neural network shows great capacity in image classification problem but its application in shoeprint retrieval is not yet investigated. This paper presents an application of VGG16 network as feature extractor in shoeprint retrieval and a data augmentation method to fine-tune the neural network with a very small database. Our method shows a much better performance compared with state-of-the-art methods on a same database with crime-scene-like shoeprints.