Retrieval of striated toolmarks using convolutional neural networks
The authors propose TripNet as method for calculating similarities between striated toolmark images. The objective for this system is detecting and comparing characteristics of the tools while being invariant to varying parameters like angle of attack, substrate material, and lighting conditions. Instead of designing a handcrafted feature extractor customised for this task, the authors propose the use of a convolutional neural network. With the proposed system, one-dimensional profiles extracted from images of striated toolmarks are mapped into an embedding. The system is trained by minimising a triplet loss function, so that a similarity measure is defined by the L 2 distance in this embedding. The performance is evaluated on the NFI Toolmark database containing 300 striated toolmarks of screwdrivers published by the Netherlands Forensic Institute. The system proposed is able to adapt to a large range of angles of attack, achieving a mean average precision of 0.95 for toolmark comparisons with differences in angle of attack of 15 ∘ – 45 ∘ . Furthermore, four different triplet selection approaches are proposed and their effect on the retrieval of toolmarks from a database of unseen tools is evaluated in detail.