The creation of publicly available image databases for the signal processing community is a very time-consuming, yet immensely valuable task, enabling scientific progress by providing the opportunity of an objective comparison and reproduction of results. This paper presents for the first time a public research collection of high-resolution latent fingerprint time series for age estimation, captured from a pool of 116 different test subjects. It comprises ten different sets with a total of 2,618 time series (117,384 scans), varying between capturing devices (CWL and CLSM), data types (intensity versus topography), aging periods (short-term aging: 24 h, long-term aging: 0.5 - 3 years) and resolutions (1,270 - 180,142 ppi). Most series are annotated with donor information (age and gender) and capturing conditions (scan parameters, ambient temperature, and humidity). The data are anonymized (using partial prints only) and an organizational revocation mechanism is included to assure non-identifiability of donors in the future. Baseline results for age estimation on all ten sets are provided in the form of correlation coefficients and machine-learning based age estimation (kappa), using 19 features from prior feature spaces as well as new ones (Tamura contrast, Benford's law, and improved dust feature). Classification results exhibit kappa values between 0.51 and 0.85, highlighting the progress made in this very challenging area in recent years and also emphasizing the need of future studies on the issue.

The aim of this paper is to discuss selected aspects of the emerging trend of digitization in the field of crime scene forensics, known as 'digitized crime scene forensics'. This work summarizes recent findings in the field and discusses the current state of transfer from the analogue to the digital domain. The trace types of latent fingerprints, fibers as well as firearm and lock picking related toolmarks are addressed in respect to the major steps of acquisition, preprocessing, feature extraction and decision making, as support for the subjective expert assessment. Based on the findings, challenges are identified to provide future directions on the issue and to stimulate an increased research in this area. Overall, it can be concluded that a complete digital and automated processing pipeline is missing for most of the considered trace types. The introduction of first digital processing schemes has enabled certain novel, so far not-addressed opportunities, such as separation of overlapped prints, age estimation as well as the application of topographic data and has been able to provide first promising results. But even if parts of this pipeline have been recently automated in first studies, significant challenges remain, such as selecting suitable capturing devices and processing methods, identifying characteristic features and classification strategies as well as employing comprehensive test sets. Most of all, the final classification performance is often not yet good enough to achieve the quality of results obtained in manual investigations, and a significant research effort is required to address the specific needs of numerous trace types and investigation objectives.

In forensic applications, traces are often hard to detect and segment from challenging substrates at crime scenes. In this paper, we propose to use the temporal domain of forensic signals as a novel feature space to provide additional information about a trace. In particular we introduce a degree of persistence measure and a protocol for its computation, allowing for a flexible extraction of time domain information based on different features and approximation techniques. At the example of latent fingerprints on semi-/porous surfaces and a CWL sensor, we show the potential of such approach to achieve an increased performance for the challenge of separating prints from background. Based on 36 earlier introduced spectral texture features, we achieve an increased separation performance (0.01 ≤ Δκ ≤ 0.13, respective 0.6% to 6.7%) when using the time domain signal instead of spatial segmentation. The test set consists of 60 different prints on photographic-, catalogue- and copy paper, acquired in a sequence of ten times. We observe a dependency on the used surface as well as the number of consecutive images and identify the accuracy and reproducibility of the capturing device as the main limitation, proposing additional steps for even higher performances in future work.

Non-invasive high-resolution Chromatic White Light (CWL) measurement devices offer great potential for solving the challenge of latent fingerprints age determination. In this paper, we place 40 prints from different subjects on hard disk platters and capture them from three different indoor locations every week over 1.5 years, acquiring high-resolution time series (10 μm and 20 μm). In contrast to prior findings from Popa et al. (using glass substrates) we show that the ridge thickness of our very precise images does not significantly decrease over time (test goal 1). We furthermore show that pores exhibit a significant loss in contrast and contour, which might lead to the impression of becoming bigger and fewer (test goal 2). Computing the contrast based Binary Pixel aging feature (test goal 3), we observe very characteristic results, leading to the conclusion that the dominant aging property seems to be an overall loss of image contrast rather than a specific change of ridge thickness or pore size. Comparing our findings between three different indoor locations (test goal 4) and discussing them from a police point of view, we conclude that sweat composition, environmental influences and scan parameters have a significant impact on fingerprints long-term aging.