Marcus Leich

Non-destructive forensic latent fingerprint acquisition with chromatic white light sensors

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.

Benchmarking contact-less surface measurement devices for fingerprint acquisition in forensic investigations: Results for a differential scan approach with a chromatic white light sensor

With the advent of new contact-less sensors for forensic investigations of latent fingerprint traces, the authors see the need for a benchmarking framework to evaluate existing devices and promising combinations of data acquisition and signal processing techniques. This paper extends the existing benchmarking framework from [1] by categorizing it into properties from a forensic point-of-view (end-user) and a technical point-of-view (scientific-user) and applies a known differential image technique for the subjective evaluation of which traces are visible. We show exemplary results for a chromatic white light (CWL) sensor for the surface quality assessment, using and comparing the experimental setup of 10 surfaces from [1] and additional 10 surfaces, including real-world objects, to determine its potential for detecting latent fingerprints. Using a differential image approach, the particular influence of sensor noise signals is analyzed, showing that this differential approach cannot always be considered as an ideal filter for fingerprint pattern detection.

A first framework for the development of age determination schemes for latent biometric fingerprint traces using a chromatic white light (CWL) sensor

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.

Revised benchmarking of contact-less fingerprint scanners for forensic fingerprint detection: challenges and results for chromatic white light scanners (CWL)

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.

Optical techniques: using coarse and detailed scans for the preventive acquisition of fingerprints with chromatic white-light sensors

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.

User discrimination in automotive systems

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.

Automated forensic fingerprint analysis: a novel generic process model and container format

The automated forensic analysis of latent fingerprints poses a new challenge. While for the pattern recognition aspects involved, the required processing steps can be related to fingerprint biometrics, the common biometric model needs to be extended to face the variety of characteristics of different surfaces and image qualities and to keep the chain of custody. Therefore, we introduce a framework for automated forensic analysis of latent fingerprints. The framework consists of a generic process model for multi-branched process graphs w.r.t. security aspects like integrity, authenticity and confidentiality. It specifies a meta-model to store all necessary data and operations in the process, while keeping the chain of custody. In addition, a concept for a technical implementation of the meta-model is given, to build a container format, which suits the needs of an automated forensic analysis in research and application.

Machine-assisted verification of latent fingerprints: first results for nondestructive contact-less optical acquisition techniques with a CWL sensor

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.