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Institute
An innovative net-based methodology to integrate qualitative and quantitative analysis of distributed software systems is outlined, and an on-going prototype implementation of a related graphic-oriented tool kit is sketched. The proposed method combines qualitative analysis, monitoring and testing as well as quantitative analysis on the basis of a net-based intermediate representation of the distributed software system under consideration. All transformations (from the distributed software system into a first Petri net model, and between the different kinds of net models) can be made formally, and therefore automated to a high degree. The evaluation of quantitative properties is based on so-called object nets which are obtained by a property-preserving structural compression and quantitative expansion of the qualitative model. In this way, the frequency and delay attributes necessary to generate quantitative models are provided by the monitoring and testing component.
Technologies for advancing and supporting criminalistic forensic are an upcoming challenge with rising importance within the domain of multimedia security and forensic. For example the acquisition and automated analysis of latent fingerprints, using high resolution 3D scanners, appears to be a promising area of research. Within the paper we will give a detailed demarcation of biometric and forensic fingerprint analysis. We will introduce the aim of a partly automated process for forensic fingerprint acquisition, detection, and processing. Based on the idea of splitting the overall scan process into a fast, low resolution coarse scan and a high resolution detailed scan, different approaches and algorithms has to be evaluated, whether they are feasible for detecting latent fingerprints. Referring to the subject of biometrics and emerging applications, this work will show an approach of a novel Fourier-based spectral texture feature for detecting latent fingerprints in low resolution grey-scale images. Our work is based on data that is acquired using a chromatic white light sensor (CWL), which provides intensity and topographic information of the scanned surface areas. While using a very limited set of different surfaces so far, our first experiments have shown good results on flat and non structured surfaces. Using the presented feature, it is possible to detect latent fingerprints on these surfaces. Even on slightly structured surfaces, like wood imitation, the application of the spectral density feature yields promising results. However the first evaluation of the spectral density feature has also shown some serious limitations of its use on low resolution images.
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.
Application of non-invasive scan technologies for acquisition of latent fingerprints promise a better support of forensic and dactyloscopic experts when securing evidence at crime scenes. Furthermore, non-destructive acquisition preserve the chance of subsequent chemical and forensic analysis of left residue. Based on results of an ongoing research project with sensor industry partners, this paper presents a collection of 28 statistical, gradient-, and spectral density-based features for latent fingerprint detection using low resolution scans. Within this work a chromatic white light (CWL) sensor is used for image acquisition. Furthermore, based on concepts of biometric fusion, a taxonomy for possible fusion strategies is presented and very first results for three different strategies on decision level are discussed. Experimental evaluation is performed based on scans of 1680 latent fingerprints on three different surfaces. The results show very good performance on planar, non-absorbing surfaces with uniform reflection characteristics with an detection rate of 2.51% in the best case. On the other hand difficulties are arising from surfaces with non-uniform/predictable reflection characteristics.
In forensic investigations, the recovering of latent fingerprints is one of the most essential issues. Driven by human experts, today this process is very time consuming. An automation of both examination of suspicious areas and acquisition of fingerprints lead on the one hand to the covering of larger surfaces and on the other hand to significant speed up of the evidence collection. This work presents an experimental study on capabilities of chromatic white-light sensor (CWL) regarding the contact-less localization of latent fingerprints on differently challenging substrates. The fully automatic CWL-based system is implemented from the acquisition through the feature extraction right up to the classification. The key objective of the work is to develop a methodological approach for the quantitative evaluation of the localization success. Based on the proposed performance measures, the optimal system parameters such as scan resolution, extracted features and classification scheme are specified dependent on the surface material. Our experiments from an actual project with the sensor industry partner show convincing localization performance on easy-to-localize and adequate performance on moderate-to-localize substrates. The hard-to-localize substrates require further improvements of the localization system.
The technology-aided support of forensic experts while investigating crime scenes and collecting traces becomes a more and more important part in the domains of image acquisition and signal processing. The manual lifting of latent fingerprints using conventional methods like the use of carbon black powder is time-consuming and very limited in its scope of application. New technologies for a contact-less and non-invasive acquisition and automatic processing of latent fingerprints, promise the possibilities to inspect much more and larger surface areas and can significantly simplify and speed up the workflow. Furthermore, it allows multiple investigations of the same trace, subsequent chemical analysis of the residue left behind and the acquisition of latent fingerprints on sensitive surfaces without destroying the surface itself. In this work, a FRT MicroProf200 surface measurement device equipped with a chromatic white-light sensor CWL600 is used. The device provides a gray-scale intensity image and 3D-topography data simultaneously. While large area scans are time-consuming, the detection and localization of finger traces are done based on low-resolution scans. The localized areas are scanned again with higher resolution. Due to the broad variety of different surface characteristics the fingerprint pattern is often overlaid by the surface structure or texture. Thus, image processing and classification techniques are proposed for validation and visualization of ridge lines in high-resolution scans. Positively validated regions containing complete or sufficient partial fingerprints are passed on to forensic experts. The experiments are provided on a set of three surfaces with different reflection and texture characteristics, and fingerprints from ten different persons.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).