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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.

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.

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.