High quality training materials to detect printed fingerprints: Benchmarking three different aquisition sensors producing printing templates

Abstract

Schwarz's technique for printing amino acid solutions introduces the possibility of printing large quantities of latent fingerprints for crime scene investigation quality assurance. Nevertheless his technique also unintentionally creates the possibility of leaving printed fingerprints at crime scenes. To help identify those false fingerprints, in our paper we extend the printing pipeline, for training investigators and detection methods. Furthermore, we propose subjective and objective evaluation approaches and first tendencies for boundary ranges for objective evaluation metrics. In particular we use digitized real latent fingerprints as printing source (= template) and different contactless sensors (two different chromatic white light sensors, FRT CWL 600, FRT CWL 1mm, and a confocal microscope Keyence VK-X105) for their acquisition. For the examination of the printed fingerprints one subjective and two objective evaluation approaches are introduced as well as a first tendency for boundary ranges of the objective approach. A Canon PIXMA IP 4600 is used for printing and the Keyence VK-X105 acquires the untreated printed fingerprints. Our benchmarking results show that the acquisition sensor Keyence VK-X105 leads to the highest quality of printed fingerprints. In respect to the boundary ranges our suggested first tendency is: correlation value with 20x-objective: Best = [0,...,0.1150], Average = [0.1151,...,0.1258], Worst = [0.1259,...,1]. With 50x-objective: Best = [0,...,0.1299], Average = [0.1300,..., 0.1443], Worst = [0.1444,...,1]. And for the average value with 20x-objective: Best = [0,...,0.0171], Average = [0.0172,...,0.0260], Worst = [0.0261,...,1]. And with 50x-objective: Best = [0,...,0.0299], Average = [0.0300,...,0.0470], Worst = [0.0471,...,1].