Friction ridge skin - Automated Fingerprint Identification System (AFIS)

This contribution describes the development and the forensic use of automated fingerprint identification systems (AFISs). AFISs were initially developed in order to overcome the limitations of the paper-based fingerprint collections, by digitizing the ten-print cards in computerized databases and to translate the manual pattern classification into computer-friendly codes. Then, technologies to automate the fingerprint feature extraction and comparison were developed, and AFISs were implemented on a large scale in order to improve the process of identification of repetitive offenders based on the ten-print cards. Further development of the fingerprint biometric technology allowed for the inclusion of palmprint reference databases and for the processing of fingermarks and palmmarks with, as a result, the partial automation of the forensic investigation and intelligence process. In the field of AFIS, the challenges for the future call for further automation of the feature extraction from low-quality fingerprint and fingermark images, for more transparency in the processes, for the improvement of the interoperability of the systems on a global level and the combination of biometric modalities as well as for the use of fingerprint biometric technology and scientific methodology, to further develop the forensic friction ridge evaluation process

Hierarchical mixture models for assessing fingerprint individuality

The study of fingerprint individuality aims to determine to what extent a fingerprint uniquely identifies an individual. Recent court cases have highlighted the need for measures of fingerprint individuality when a person is identified based on fingerprint evidence. The main challenge in studies of fingerprint individuality is to adequately capture the variability of fingerprint features in a population. In this paper hierarchical mixture models are introduced to infer the extent of individualization. Hierarchical mixtures utilize complementary aspects of mixtures at different levels of the hierarchy. At the first (top) level, a mixture is used to represent homogeneous groups of fingerprints in the population, whereas at the second level, nested mixtures are used as flexible representations of distributions of features from each fingerprint. Inference for hierarchical mixtures is more challenging since the number of unknown mixture components arise in both the first and second levels of the hierarchy. A Bayesian approach based on reversible jump Markov chain Monte Carlo methodology is developed for the inference of all unknown parameters of hierarchical mixtures. The methodology is illustrated on fingerprint images from the NIST database and is used to make inference on fingerprint individuality estimates from this population.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS266 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Performing the Union: the Prüm Decision and the European dream

In 2005, seven European countries signed the so-called Prüm Treaty to increase transnational collaboration in combating international crime, terrorism and illegal immigration. Three years later, the Treaty was adopted into EU law. EU member countries are obliged to have systems in place to allow authorities of other member states access to nationally held data on DNA, fingerprints, and vehicles by August 2011. In this paper, we discuss the conditions of possibility for the Prüm network to emerge, and argue that rather than a linear story of technological and political convergence and harmonisation, the (hi)story of Prüm is heterogeneous and patchy. This is reflected also in the early stages of implementing the Prüm Decision which proves to be more difficult than it was hoped by the drivers of the Prüm process. In this sense, the Prüm network sits uncomfortably with success stories of forensic science (many of which served the goal of justifying the expansion of technological and surveillance systems). Instead of telling a story of heroic science, the story of Prüm articulates the European dream: One in which goods, services, and people live and travel freely and securely

A first step to accelerating fingerprint matching based on deformable minutiae clustering

Fingerprint recognition is one of the most used biometric methods for authentication. The identification of a query fingerprint requires matching its minutiae against every minutiae of all the fingerprints of the database. The state-of-the-art matching algorithms are costly, from a computational point of view, and inefficient on large datasets. In this work, we include faster methods to accelerating DMC (the most accurate fingerprint matching algorithm based only on minutiae). In particular, we translate into C++ the functions of the algorithm which represent the most costly tasks of the code; we create a library with the new code and we link the library to the original C# code using a CLR Class Library project by means of a C++/CLI Wrapper. Our solution re-implements critical functions, e.g., the bit population count including a fast C++ PopCount library and the use of the squared Euclidean distance for calculating the minutiae neighborhood. The experimental results show a significant reduction of the execution time in the optimized functions of the matching algorithm. Finally, a novel approach to improve the matching algorithm, considering cache memory blocking and parallel data processing, is presented as future work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech