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Latent fingerprints provide vital information in modern crime scene investigation. On frequently touched surfaces the fingerprints may overlap which poses a major problem for forensic analysis. In order to make such overlapping fingerprints available for analysis, they have to be separated. An additional evaluation of the sequence in which the fingerprints were brought onto the surface can help to reconstruct the progression of events. Advances in both tasks can considerably aid crime investigation agencies and are the subject of this work. Here, a statistical approach, initially devised for the separation of overlapping text patterns by Tonazzini et al.,1 is employed to separate overlapping fingerprints. The method involves a maximum a posteriori estimation of the single fingerprints and the mixing coefficients, computed by an expectation-maximization algorithm. A fingerprint age determination feature based on corrosion is evaluated for sequence estimation. The approaches are evaluated using 30 samples of overlapping latent fingerprints on two different substrates. The fingerprint images are acquired with a non-destructive chromatic white light surface measurement device, each sample containing exactly two fingerprints that overlap in the center of the image. Since forensic investigations rely on manual assessment of acquired fingerprints by forensics experts, a subjective scale ranging from 0 to 8 is used to rate the separation results. Our results indicate that the chosen method can separate overlapped fingerprints which exhibit strong differences in contrast, since results gradually improve with the growing contrast difference of the overlapping fingerprints. Investigating the effects of corrosion leads to a reliable determination of the fingerprints' sequence as the timespan between their leaving increases.

A machine-assisted analysis of traces from crime scenes might be possible with the advent of new high-resolution non-destructive contact-less acquisition techniques for latent fingerprints. This requires reliable techniques for the automatic extraction of fingerprint features from latent and exemplar fingerprints for matching purposes using pattern recognition approaches. Therefore, we evaluate the NIST Biometric Image Software for the feature extraction and verification of contact-lessly acquired latent fingerprints to determine potential error rates. Our exemplary test setup includes 30 latent fingerprints from 5 people in two test sets that are acquired from different surfaces using a chromatic white light sensor. The first test set includes 20 fingerprints on two different surfaces. It is used to determine the feature extraction performance. The second test set includes one latent fingerprint on 10 different surfaces and an exemplar fingerprint to determine the verification performance. This utilized sensing technique does not require a physical or chemical visibility enhancement of the fingerprint residue, thus the original trace remains unaltered for further investigations. No particular feature extraction and verification techniques have been applied to such data, yet. Hence, we see the need for appropriate algorithms that are suitable to support forensic investigations.

Since decades, the age determination of latent fingerprint traces left at crime scenes is a challenge to forensic investigators, since fingerprint traces can often only be used in a lawsuit if they can be assigned to the specific time interval of a crime taking place. In this paper, we suggest a six-step framework on how an age determination scheme might be developed for a given application scenario using optical and non-invasive image sensory. We explain and discuss each step of such framework, using three different aging features (the binary pixel feature based on prior work as well as the novel aging features corrosion blob size and corrosion blob amount), which are based on the loss of contrast of a fingerprint on a hard disk platter surface as well as on corrosion properties of fingerprint residue applied to a copper coin surface. We furthermore evaluate different aspects of the scheme in practical experiments, to show its feasibility and conclude, that the steps 1, 3, 5 and 6 of the framework are comparatively easy to be realized, whereas the steps 2 (developing new aging features) and 4 (determining all significant influences on a given aging feature) are challenging, yet possible.

The preventive application of automated latent fingerprint acquisition devices can enhance the Homeland Defence, e.g. by improving the border security. Here, contact-less optical acquisition techniques for the capture of traces are subject to research; chromatic white light sensors allow for multi-mode operation using coarse or detailed scans. The presence of potential fingerprints could be detected using fast coarse scans. Those Regions-of- Interest can be acquired afterwards with high-resolution detailed scans to allow for a verification or identification of individuals. An acquisition and analysis of fingerprint traces on different objects that are imported or pass borders might be a great enhancement for security. Additionally, if suspicious objects require a further investigation, an initial securing of potential fingerprints could be very useful. In this paper we show current research results for the coarse detection of fingerprints to prepare the detailed acquisition from various surface materials that are relevant for preventive applications.

Non-destructive latent fingerprint acquisition is an emerging field of research, which, unlike traditional methods, makes latent fingerprints available for additional verification or further analysis like tests for substance abuse or age estimation. In this paper a series of tests is performed to investigate the overall suitability of a high resolution off-the-shelf chromatic white light sensor for the contact-less and non-destructive latent fingerprint acquisition. Our paper focuses on scanning previously determined regions with exemplary acquisition parameter settings. 3D height field and reflection data of five different latent fingerprints on six different types of surfaces (HDD platter, brushed metal, painted car body (metallic and non-metallic finish), blued metal, veneered plywood) are experimentally studied. Pre-processing is performed by removing low-frequency gradients. The quality of the results is assessed subjectively; no automated feature extraction is performed. Additionally, the degradation of the fingerprint during the acquisition period is observed. While the quality of the acquired data is highly dependent on surface structure, the sensor is capable of detecting the fingerprint on all sample surfaces. On blued metal the residual material is detected; however, the ridge line structure dissolves within minutes after fingerprint placement.

Mobile contact-less fingerprint scanners can be very important tools for the forensic investigation of crime scenes. To be admissible in court, data and the collection process must adhere to rules w.r.t. technology and procedures of acquisition, processing and the conclusions drawn from that evidence. Currently, no overall accepted benchmarking methodology is used to support some of the rules regarding the localisation, acquisition and pre-processing using contact-less fingerprint scanners. Benchmarking is seen essential to rate those devices according to their usefulness for investigating crime scenes. Our main contribution is a revised version of our extensible framework for methodological benchmarking of contact-less fingerprint scanners using a collection of extensible categories and items. The suggested main categories describing a contact-less fingerprint scanner are properties of forensic country-specific legal requirements, technical properties, application-related aspects, input sensory technology, pre-processing algorithm, tested object and materials. Using those it is possible to benchmark fingerprint scanners and describe the setup and the resulting data. Additionally, benchmarking profiles for different usage scenarios are defined. First results for all suggested benchmarking properties, which will be presented in detail in the final paper, were gained using an industrial device (FRT MicroProf200) and conducting 18 tests on 10 different materials.

The recently developed dual-view touch screens, which are announced to be installed in cars in a near future, give rise to completely new challenges in human-machine interaction. The automotive system should be able to identify if the driver or the passenger is currently interacting with the touch screen to provide a correct response to the touch. The optical devices, due to availability, acceptance by the users and multifunctional usage, approved to be the most appropriate sensing technology for driver/passenger discrimination. In this work the prototypic optical user discrimination system is implemented in the car simulator and evaluated in the laboratory environment with entirely controlled illumination. Three tests were done for this research. One of them examined if the near-infrared illumination should be switched on around the clock, the second one if there is a difference in discrimination performance between day, twilight and night conditions, and the third one examined how the intensive directional lighting influences the performance of the implemented user discrimination algorithm. Despite the high error rates, the evaluation results show that very simple computer vision algorithms are able to solve complicated user discrimination task. The average error rate of 10.42% (daytime with near-infrared illumination) is a very promising result for optical systems.

According to the European Commission around 200,000 counterfeit Euro coins are removed from circulation every year. While approaches exist to automatically detect these coins, satisfying error rates are usually only reached for low quality forgeries, so-called "local classes". High-quality minted forgeries ("common classes") pose a problem for these methods as well as for trained humans. This paper presents a first approach for statistical analysis of coins based on high resolution 3D data acquired with a chromatic white light sensor. The goal of this analysis is to determine whether two coins are of common origin. The test set for these first and new investigations consists of 62 coins from not more than five different sources. The analysis is based on the assumption that, apart from markings caused by wear such as scratches and residue consisting of grease and dust, coins from equal origin have a more similar height field than coins from different mints. First results suggest that the selected approach is heavily affected by influences of wear like dents and scratches and the further research is required the eliminate this influence. A course for future work is outlined.