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Interactions between latent fingermarks, deposition surfaces and development agents
This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.Fingerprints have provided a crucial source of forensic evidence for well over a century. Their power lies in an inherent ability for human identification and
individualisation, which is based on two fundamental properties: uniqueness and
lifelong permanence. Latent fingermarks represent by far the most evidentially
common and challenging form of deposition, whereby an invisible copy of the unique
friction ridge fingertip pattern is left as an amalgamated secretory residue on any
surface that is touched. Dry powder dusting, the first and most iconic method for
visualising or developing these deposits, was developed in the latter part of the 19th
Century. In the period since, a great number of additional techniques, utilising
physical, chemical and optical interactions in isolation or combined, have been
devised for the same purpose. By selecting the correct technique in the correct order,
it is now possible to extract significant print details from an unprecedented variety of surfaces. In the UK, such operational choices are recommended via Home Office
issued protocol tables, which offer an optimum guide based on substrate type,
substrate properties and fingermark conditions. Development technique specificity has improved in the last half-century alongside increased biochemical understanding of residue composition, however, the shear variety of potential deposition substrates that exist within a heavily industrialised world inevitably causes disparities in efficiency, even within single protocol classifications. These effects are compounded by the enormous potential for pre- and post-deposition residue composition variation, relating to donor factors (age, sex, diet, lifestyle, etc.) and time dependant changes (environmental, biological, etc.) respectively. As a result, routine technique
application can cause sub-optimal development. This research utilises high resolution imaging and analysis techniques to demonstrate how subtle surface chemistry and topography features can selectively influence routine technique efficiency within a single protocol classification (smooth, nonporous plastics). Titanium dioxide, a widely used white pigment, has been shown as prevalent in a range of polymers following SEM and EDX analysis, either in a patchy or ubiquitous distribution. SEM analysis demonstrates a strong interaction between the pigment and carbon powder suspension, which causes detrimental overdevelopment effects in off-ridge areas. ToF-SIMS mapping of a Formica
substrate places a significant amount of patchy distributed titanium dioxide in the top 30nm of the surface. Mapping also indicated the presence of an aluminosilicate
pigment coating; however, it’s involvement in the possible surface potential or surface
energy interaction mechanism is unknown The effects of linear surface features, which have previously been implicated in off-ridge cyanoacrylate overdevelopment on two operationally relevant polymers, were also analysed by creating a silicon wafer model for micro-FTIR analysis. Fingermark residues, including hydroxyl groups, have been shown to migrate significant distances along induced scratches in the model substrate over a 48hr period. It is likely that observed overdevelopment along large valley-like features (uPVC) and scratches (polyethylene) in the operationally
relevant polymers is caused by a similar migration of residues.This work is funded by the UK Home
Office project 7121939
Indexing techniques for fingerprint and iris databases
This thesis addresses the problem of biometric indexing in the context of fingerprint and iris databases. In large scale authentication system, the goal is to determine the identity of a subject from a large set of identities. Indexing is a technique to reduce the number of candidate identities to be considered by the identification algorithm. The fingerprint indexing technique (for closed set identification) proposed in this thesis is based on a combination of minutiae and ridge features. Experiments conducted on the FVC2002 and FVC2004 databases indicate that the inclusion of ridge features aids in enhancing indexing performance. The thesis also proposes three techniques for iris indexing (for closed set identification). The first technique is based on iriscodes. The second technique utilizes local binary patterns in the iris texture. The third technique analyzes the iris texture based on a pixel-level difference histogram. The ability to perform indexing at the texture level avoids the computational complexity involved in encoding and is, therefore, more attractive for iris indexing. Experiments on the CASIA 3.0 database suggest the potential of these schemes to index large-scale iris databases
Facilitating sensor interoperability and incorporating quality in fingerprint matching systems
This thesis addresses the issues of sensor interoperability and quality in the context of fingerprints and makes a three-fold contribution. The first contribution is a method to facilitate fingerprint sensor interoperability that involves the comparison of fingerprint images originating from multiple sensors. The proposed technique models the relationship between images acquired by two different sensors using a Thin Plate Spline (TPS) function. Such a calibration model is observed to enhance the inter-sensor matching performance on the MSU dataset containing images from optical and capacitive sensors. Experiments indicate that the proposed calibration scheme improves the inter-sensor Genuine Accept Rate (GAR) by 35% to 40% at a False Accept Rate (FAR) of 0.01%. The second contribution is a technique to incorporate the local image quality information in the fingerprint matching process. Experiments on the FVC 2002 and 2004 databases suggest the potential of this scheme to improve the matching performance of a generic fingerprint recognition system. The final contribution of this thesis is a method for classifying fingerprint images into 3 categories: good, dry and smudged. Such a categorization would assist in invoking different image processing or matching schemes based on the nature of the input fingerprint image. A classification rate of 97.45% is obtained on a subset of the FVC 2004 DB1 database
Distributed authentication to preserve privacy through smart card based biometric matching
Bibliography: pages 135-139.This thesis focuses on privacy concerns, specifically those relating to the storage and use of biometrics. These concerns result from the fact that biometric information is unique. This uniqueness makes the biometric a very strong identifier increasing the possibility that it could be used to monitor an individual's activities. An expert can extract considerable information from a biometric scan, ranging from the age or gender to whether the individual has certain diseases
Inkjet-Printed Carbon Nanotubes for Fabricating a Spoof Fingerprint on Paper.
A spoof fingerprint was fabricated on paper and applied for a spoofing attack to unlock a smartphone on which a capacitive array of sensors had been embedded with a fingerprint recognition algorithm. Using an inkjet printer with an ink made of carbon nanotubes (CNTs), we printed a spoof fingerprint having an electrical and geometric pattern of ridges and furrows comparable to that of the real fingerprint. With this printed spoof fingerprint, we were able to unlock a smartphone successfully; this was due to the good quality of the printed CNT material, which provided electrical conductivities and structural patterns similar to those of the real fingerprint. This result confirms that inkjet-printing CNTs to fabricate a spoof fingerprint on paper is an easy, simple spoofing route from the real fingerprint and suggests a new method for outputting the physical ridges and furrows on a two-dimensional plane
Feature Fusion for Fingerprint Liveness Detection
For decades, fingerprints have been the most widely used biometric trait in identity
recognition systems, thanks to their natural uniqueness, even in rare cases such as
identical twins. Recently, we witnessed a growth in the use of fingerprint-based
recognition systems in a large variety of devices and applications. This, as a consequence,
increased the benefits for offenders capable of attacking these systems. One
of the main issues with the current fingerprint authentication systems is that, even
though they are quite accurate in terms of identity verification, they can be easily
spoofed by presenting to the input sensor an artificial replica of the fingertip skin’s
ridge-valley patterns.
Due to the criticality of this threat, it is crucial to develop countermeasure
methods capable of facing and preventing these kind of attacks. The most effective
counter–spoofing methods are those trying to distinguish between a "live" and a
"fake" fingerprint before it is actually submitted to the recognition system. According
to the technology used, these methods are mainly divided into hardware and software-based
systems. Hardware-based methods rely on extra sensors to gain more pieces
of information regarding the vitality of the fingerprint owner. On the contrary,
software-based methods merely rely on analyzing the fingerprint images acquired
by the scanner. Software-based methods can then be further divided into dynamic,
aimed at analyzing sequences of images to capture those vital signs typical of a real
fingerprint, and static, which process a single fingerprint impression. Among these
different approaches, static software-based methods come with three main benefits.
First, they are cheaper, since they do not require the deployment of any additional
sensor to perform liveness detection. Second, they are faster since the information
they require is extracted from the same input image acquired for the identification
task. Third, they are potentially capable of tackling novel forms of attack through an
update of the software. The interest in this type of counter–spoofing methods is at the basis of this
dissertation, which addresses the fingerprint liveness detection under a peculiar
perspective, which stems from the following consideration. Generally speaking, this
problem has been tackled in the literature with many different approaches. Most of
them are based on first identifying the most suitable image features for the problem
in analysis and, then, into developing some classification system based on them. In
particular, most of the published methods rely on a single type of feature to perform
this task. Each of this individual features can be more or less discriminative and often
highlights some peculiar characteristics of the data in analysis, often complementary
with that of other feature. Thus, one possible idea to improve the classification
accuracy is to find effective ways to combine them, in order to mutually exploit their
individual strengths and soften, at the same time, their weakness. However, such a
"multi-view" approach has been relatively overlooked in the literature.
Based on the latter observation, the first part of this work attempts to investigate
proper feature fusion methods capable of improving the generalization and robustness
of fingerprint liveness detection systems and enhance their classification strength.
Then, in the second part, it approaches the feature fusion method in a different way,
that is by first dividing the fingerprint image into smaller parts, then extracting an
evidence about the liveness of each of these patches and, finally, combining all these
pieces of information in order to take the final classification decision.
The different approaches have been thoroughly analyzed and assessed by comparing
their results (on a large number of datasets and using the same experimental
protocol) with that of other works in the literature. The experimental results discussed
in this dissertation show that the proposed approaches are capable of obtaining
state–of–the–art results, thus demonstrating their effectiveness
A literature analysis examining the potential suitability of terahertz imaging to detect friction ridge detail preserved in the imprimatura layer of oil-based, painted artwork
This literature analysis examines terahertz (THz) imaging as a non-invasive tool for the imaging of friction ridge detail from the first painted layer (imprimatura) in multilayered painted works of art. The paintings of interest are those created utilizing techniques developed during the Renaissance and still in use today. The goal of analysis serves to answer two questions. First, can THz radiation penetrate paint layers covering the imprimatura to reveal friction ridge information? Secondly, can the this technology
recover friction ridge detail such that the fine details are sufficiently resolved to provide
images suitable for comparison and identification purposes.
If a comparison standard exists, recovered friction ridge detail from this layer can be used to establish linkages to an artist or between works of art. Further, it can be added to other scientific methods currently employed to assist with the authentication efforts of unattributed paintings.
Flanked by the microwave and far-infrared edges, THz straddles the electronic and optic perspectives of the electromagnetic spectrum. As a consequence, this range is imparted with unique and useful properties. Able to penetrate and image through many opaque materials, its non-ionizing radiation is an ideal non-destructive technique that provides visual information from a painting’s sub-strata. Imaging is possible where refractive index differences exist between different paint layers.
Though it is impossible, at present, to determine when a fingerprint was deposited, one can infer approximately when a print was created if it is recovered from the imprimatura layer of a painting, and can be subsequently attributed to a known source. Fingerprints are unique, a person is only able to deposit prints while their physical body is intact and thus, in some cases, the multiple layer process some artists use in their work may be used to the examiner’s advantage.
Impressions of friction ridge detail have been recorded on receiving surfaces from human hands throughout time (and have also been discovered in works of art). Yet, the potential to associate those recorded impressions to a specific individual was only realized just over one hundred years ago. Much like the use of friction ridge skin, the relatively recently discovered THz range is now better understood; its tremendous potential unlocked by growing research and technology designed to exploit its unique properties
Prevalence of Pores in Latent Fingerprints
Of the many biometric traits recognized today, fingerprints are the most prevalent and familiar. The analysis of fingerprints involves level 1, level 2, and/or level 3 detail in the identification of a potential match. Traditionally, fingerprint matching was completely performed by hand, utilizing the ACE-V method. Thanks to the development of rapidly evolving technology, fingerprint matching has become an automated procedure through the use of fingerprint matching algorithms. In the literature, there has been an increase in the interest of developing Automatic Fingerprint Identification System (AFIS) algorithms that include level 3 details in the matching process. These studies have utilized live scanned and/or inked fingerprints, rather than latent fingerprints. However, practical use of AFIS algorithms involves unknown fingerprints, such as those collected at crime scenes, which are often latent in nature. In addition, research has also found that there is a wide variety in size and shape of pore structure, making automatic detection of pores difficult. The resultant quality of latent fingerprints is subject to various factors at the time of deposition, such as the deposition surface, environmental conditions, and composition of the fingerprint itself. Consequently, these factors, in addition to the inherent variance in pore structure, may very well affect the observance and use of level 3 details within a fingerprint. If the prevalence of pores proves to be unreliable and inconsistent in latent fingerprints, the push for including level 3 detail in the AFIS matching process may all be for nothing. For this reason, the effects of latent fingerprint deposition factors on pore identification needs to be considered and currently appears to be greatly under studied. In effort to begin to fill this gap in the current research, newly deposited latent fingerprints were collected and developed using both black fingerprint powder and cyanoacrylate fuming. Developed fingerprints were subsequently imaged via digital scan or digital camera, and enhanced using either Image J or Adobe\textsuperscript{\textregistered} Photoshop\textsuperscript{\textregistered}. Following image enhancement, pores were manually identified and marked using the Federal Bureau of Investigation (FBI) developed Universal Latent Workstation (ULW) software.
Qualitative assessment of the 633 fingerprints collected resulted in 380 usable fingerprints for the remainder of the study. Observations regarding pore count within the replicate fingerprint sets indicated that total pore count/presence was not consistent. The Mann Whitney U test indicated that neither development method, black fingerprint powder nor cyanoacrylate fuming, produced pore data any better or worse than the other. Lastly, assessment of pore location resulted in a greater number of similarity scores being lower than the established threshold, indicating that pore location is not as easily assessed nor interpreted as hoped
Error propagation in pattern recognition systems: Impact of quality on fingerprint categorization
The aspect of quality in pattern classification has recently been explored in the context of biometric identification and authentication systems. The results presented in the literature indicate that incorporating information about quality of the input pattern leads to improved classification performance. The quality itself, however, can be defined in a number of ways, and its role in the various stages of pattern classification is often ambiguous or ad hoc.
In this dissertation a more systematic approach to the incorporation of localized quality metrics into the pattern recognition process is developed for the specific task of fingerprint categorization. Quality is defined not as an intrinsic property of the image, but rather in terms of a set of defects introduced to it. A number of fingerprint images have been examined and the important quality defects have been identified and modeled in a mathematically tractable way. The models are flexible and can be used to generate synthetic images that can facilitate algorithm development and large scale, less time consuming performance testing. The effect of quality defects on various stages of the fingerprint recognition process are examined both analytically and empirically. For these defect models, it is shown that the uncertainty of parameter estimates, i.e. extracted fingerprint features, is the key quantity that can be calculated and propagated forward through the stages of the fingerprint classification process. Modified image processing techniques that explicitly utilize local quality metrics in the extraction of features useful in fingerprint classification, such as ridge orientation flow field, are presented and their performance is investigated
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