Benchmarking contactless acquisition sensor reproducibility for latent fingerprint trace evidence

Optical, nano-meter range, contactless, non-destructive sensor devices are promising acquisition techniques in crime scene trace forensics, e.g. for digitizing latent fingerprint traces. Before new approaches are introduced in crime investigations, innovations need to be positively tested and quality ensured. In this paper we investigate sensor reproducibility by studying different scans from four sensors: two chromatic white light sensors (CWL600/CWL1mm), one confocal laser scanning microscope, and one NIR/VIS/UV reflection spectrometer. Firstly, we perform an intra-sensor reproducibility testing for CWL600 with a privacy conform test set of artificial-sweat printed, computer generated fingerprints. We use 24 different fingerprint patterns as original samples (printing samples/templates) for printing with artificial sweat (physical trace samples) and their acquisition with contactless sensory resulting in 96 sensor images, called scan or acquired samples. The second test set for inter-sensor reproducibility assessment consists of the first three patterns from the first test set, acquired in two consecutive scans using each device. We suggest using a simple feature space set in spatial and frequency domain known from signal processing and test its suitability for six different classifiers classifying scan data into small differences (reproducible) and large differences (non-reproducible). Furthermore, we suggest comparing the classification results with biometric verification scores (calculated with NBIS, with threshold of 40) as biometric reproducibility score. The Bagging classifier is nearly for all cases the most reliable classifier in our experiments and the results are also confirmed with the biometric matching rates.