287 research outputs found
CONTACTLESS FINGERPRINT BIOMETRICS: ACQUISITION, PROCESSING, AND PRIVACY PROTECTION
Biometrics is defined by the International Organization for Standardization (ISO) as \u201cthe automated recognition of individuals based on their behavioral and biological characteristics\u201d Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA.
The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals.
Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services.
Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions.
Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used.
In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples.
Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists.
In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data.
The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications.
Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability.
The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography.
The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage
Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach
In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated.
In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data.
Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models.
In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network.
Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest
Fingerabdruckswachstumvorhersage, Bildvorverarbeitung und Multi-level Judgment Aggregation
Im ersten Teil dieser Arbeit wird Fingerwachstum
untersucht und eine Methode zur Vorhersage von Wachstum
wird vorgestellt. Die Effektivität dieser Methode wird
mittels mehrerer Tests validiert. Vorverarbeitung von
Fingerabdrucksbildern wird im zweiten Teil behandelt
und neue Methoden zur Schätzung des Orientierungsfelds
und der Ridge-Frequenz sowie zur Bildverbesserung
werden vorgestellt: Die Line Sensor Methode zur
Orientierungsfeldschätzung, gebogene Regionen zur
Ridge-Frequenz-Schätzung und gebogene Gabor Filter zur
Bildverbesserung. Multi-level Jugdment Aggregation wird
eingeführt als Design Prinzip zur Kombination mehrerer
Methoden auf mehreren Verarbeitungsstufen. Schließlich
wird Score Neubewertung vorgestellt, um Informationen
aus der Vorverarbeitung mit in die Score Bildung
einzubeziehen. Anhand eines Anwendungsbeispiels wird
die Wirksamkeit dieses Ansatzes auf den verfügbaren
FVC-Datenbanken gezeigt.Finger growth is studied in the first part of the
thesis and a method for growth prediction is presented.
The effectiveness of the method is validated in several
tests. Fingerprint image preprocessing is discussed in
the second part and novel methods for orientation field
estimation, ridge frequency estimation and image
enhancement are proposed: the line sensor method for
orientation estimation provides more robustness to
noise than state of the art methods. Curved regions are
proposed for improving the ridge frequency estimation
and curved Gabor filters for image enhancement. The
notion of multi-level judgment aggregation is
introduced as a design principle for combining
different methods at all levels of fingerprint image
processing. Lastly, score revaluation is proposed for
incorporating information obtained during preprocessing
into the score, and thus amending the quality of the
similarity measure at the final stage. A sample
application combines all proposed methods of the second
part and demonstrates the validity of the approach by
achieving massive verification performance improvements
in comparison to state of the art software on all
available databases of the fingerprint verification
competitions (FVC)
Peak annotation and data analysis software tools for mass spectrometry imaging
La metabolòmica espacial és la disciplina que estudia les imatges de les distribucions de compostos químics de baix
pes (metabòlits) a la superfície dels teixits biològics per revelar interaccions entre molècules. La imatge
d'espectrometria de masses (MSI) és actualment la tècnica principal per obtenir informació d'imatges moleculars per a
la metabolòmica espacial. MSI és una tecnologia d'imatges moleculars sense marcador que produeix espectres de
masses que conserven les estructures espacials de les mostres de teixit. Això s'aconsegueix ionitzant petites porcions
d'una mostra (un píxel) en un ràster definit a través de tota la seva superfície, cosa que dona com a resultat una
col·lecció d'imatges de distribució de ions (registrades com a relacions massa-càrrega (m/z)) sobre la mostra. Aquesta
tesi té com a objectius desenvolupar eines computacionals per a l'anotació de pics de MSI i el disseny de fluxos de
treball per a l'anàlisi estadística i multivariant de dades MSI, inclosa la segmentació espacial. El treball realitzat en
aquesta tesi es pot separar clarament en dues parts. En primer lloc, el desenvolupament d'una eina d'anotació de pics
d'isòtops i adductes adequada per facilitar la identificació de compostos de rang de massa baix. Ara podem trobar
fàcilment ions monoisotòpics als nostres conjunts de dades MSI gràcies al paquet de programari rMSIannotation. En
segon lloc, el desenvolupament de eines de programari per a l’anàlisi de dades i la segmentació espacial basades en
soft clustering per a dades MSI.La metabolómica espacial es la disciplina que estudia las imágenes de las distribuciones de compuestos químicos de
bajo peso (metabolitos) en la superficie de los tejidos biológicos para revelar interacciones entre moléculas. Las
imágenes de espectrometría de masas (MSI) es actualmente la principal técnica para obtener información de
imágenes moleculares para la metabolómica espacial. MSI es una tecnología de imágenes moleculares sin marcador
que produce espectros de masas que conservan las estructuras espaciales de las muestras de tejido. Esto se logra
ionizando pequeñas porciones de una muestra (un píxel) en un ráster definido a través de toda su superficie, lo que da
como resultado una colección de imágenes de distribución de iones (registradas como relaciones masa-carga (m/z))
sobre la muestra. Esta tesis tiene como objetivo desarrollar herramientas computacionales para la anotación de picos
en MSI y en el diseño de flujos de trabajo para el análisis estadístico y multivariado de datos MSI, incluida la
segmentación espacial. El trabajo realizado en esta tesis se puede separar claramente en dos partes. En primer lugar,
el desarrollo de una herramienta de anotación de picos de isótopos y aductos adecuada para facilitar la identificación
de compuestos de bajo rango de masa. Ahora podemos encontrar fácilmente iones monoisotópicos en nuestros
conjuntos de datos MSI gracias al paquete de software rMSIannotation.Spatial metabolomics is the discipline that studies the images of the distributions of low weight chemical compounds
(metabolites) on the surface of biological tissues to unveil interactions between molecules. Mass spectrometry imaging
(MSI) is currently the principal technique to get molecular imaging information for spatial metabolomics. MSI is a labelfree
molecular imaging technology that produces mass spectra preserving the spatial structures of tissue samples. This
is achieved by ionizing small portions of a sample (a pixel) in a defined raster through all its surface, which results in a
collection of ion distribution images (registered as mass-to-charge ratios (m/z)) over the sample. This thesis is aimed to
develop computational tools for peak annotation in MSI and in the design of workflows for the statistical and
multivariate analysis of MSI data, including spatial segmentation. The work carried out in this thesis can be clearly
separated in two parts. Firstly, the development of an isotope and adduct peak annotation tool suited to facilitate the
identification of the low mass range compounds. We can now easily find monoisotopic ions in our MSI datasets thanks
to the rMSIannotation software package. Secondly, the development of software tools for data analysis and spatial
segmentation based on soft clustering for MSI data. In this thesis, we have developed tools and methodologies to
search for significant ions (rMSIKeyIon software package) and for the soft clustering of tissues (Fuzzy c-means
algorithm)
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