Internal fingerprint extraction

Fingerprints are a non-invasive biometric that possess significant advantages. However, they are subject to surface erosion and damage; distortion upon scanning; and are vulnerable to fingerprint spoofing. The internal fingerprint exists as the undulations of the papillary junction - an intermediary layer of skin - and provides a solution to these disadvantages. Optical coherence tomography is used to capture the internal fingerprint. A depth profile of the papillary junction throughout the OCT scans is first constructed using fuzzy c-means clustering and a fine-tuning procedure. This information is then used to define localised regions over which to average pixels for the resultant internal fingerprint. When compared to a ground-truth internal fingerprint zone, the internal fingerprint zone detected automatically is within the measured bounds of human error. With a mean- squared-error of 21.3 and structural similarity of 96.4%, the internal fingerprint zone was successfully found and described. The extracted fingerprints exceed their surface counterparts with respect to orientation certainty and NFIQ scores (both of which are respected fingerprint quality assessment criteria). Internal to surface fingerprint correspondence and internal fingerprint cross correspondence were also measured. A larger scanned region is shown to be advantageous as internal fingerprints extracted from these scans have good surface correspondence (75% had at least one true match with a surface counterpart). It is also evidenced that internal fingerprints can constitute a fingerprint database. 96% of the internal fingerprints extracted had at least one corresponding match with another internal fingerprint. When compared to surface fingerprints cropped to match the internal fingerprints’ representative area and locality, the internal fingerprints outperformed these cropped surface counterparts. The internal fingerprint is an attractive biometric solution. This research develops a novel approach to extracting the internal fingerprint and is an asset to the further development of technologies surrounding fingerprint extraction from OCT scans. No earlier work has extracted or tested the internal fingerprint to the degree that this research has

Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison

Biometric recognition based on the texture along palmprint lines

Tese de Mestrado Integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

Topological analysis of discrete scalar data

This thesis presents a novel computational framework that allows for a robust extraction and quantification of the Morse-Smale complex of a scalar field given on a 2- or 3- dimensional manifold. The proposed framework is based on Forman\u27s discrete Morse theory, which guarantees the topological consistency of the computed complex. Using a graph theoretical formulation of this theory, we present an algorithmic library that computes the Morse-Smale complex combinatorially with an optimal complexity of $O(n^2)$ and efficiently creates a multi-level representation of it. We explore the discrete nature of this complex, and relate it to the smooth counterpart. It is often necessary to estimate the feature strength of the individual components of the Morse-Smale complex -- the critical points and separatrices. To do so, we propose a novel output-sensitive strategy to compute the persistence of the critical points. We also extend this wellfounded concept to separatrices by introducing a novel measure of feature strength called separatrix persistence. We evaluate the applicability of our methods in a wide variety of application areas ranging from computer graphics to planetary science to computer and electron tomography.In dieser Dissertation präsentieren wir ein neues System zur robusten Berechnung des Morse-Smale Komplexes auf 2- oder 3-dimensionalen Mannigfaltigkeiten. Das vorgestellte System basiert auf Forman’s diskreter Morsetheorie und garantiert damit die topologische Konsistenz des berechneten Komplexes. Basierend auf einer graphentheoretischer Formulierung präesentieren wir eine Bibliothek von Algorithmen, die es erlaubt, den Morse-Smale Komplex mit einer optimalen Kompliztät von $O(n^2)$ kombinatorisch zu berechnen und effizient eine mehrskalige Repräsentation davon erstellt. Wir untersuchen die diskrete Natur dieses Komplexes und vergleichen ihn zu seinem kontinuierlichen Gegenstück. Es ist häufig notwendig, die Merkmalsstärke einzelner Bestandteile des Komplexes -- der kritischen Punkte und Separatrizen -- abzuschätzen. Hierfür stellen wir eine neue outputsensitive Strategie vor, um die Persistenz von kritischen Punkten zu berechen. Wir erweitern dieses fundierte Konzept auf Separatrizen durch die Einführung des Wichtigkeitsmaßes Separatrixpersistenz. Wir evaluieren die Anwendbarkeit unserer Methoden anhand vielfältiger Anwendungen aus den Gebieten der Computergrafik, Planetologie, Computer- und Elektronentomographie

A pilot study on discriminative power of features of superficial venous pattern in the hand

The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest

Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains

Novel algorithms for 3D human face recognition

textAutomated human face recognition is a computer vision problem of considerable practical significance. Existing two dimensional (2D) face recognition techniques perform poorly for faces with uncontrolled poses, lighting and facial expressions. Face recognition technology based on three dimensional (3D) facial models is now emerging. Geometric facial models can be easily corrected for pose variations. They are illumination invariant, and provide structural information about the facial surface. Algorithms for 3D face recognition exist, however the area is far from being a matured technology. In this dissertation we address a number of open questions in the area of 3D human face recognition. Firstly, we make available to qualified researchers in the field, at no cost, a large Texas 3D Face Recognition Database, which was acquired as a part of this research work. This database contains 1149 2D and 3D images of 118 subjects. We also provide 25 manually located facial fiducial points on each face in this database. Our next contribution is the development of a completely automatic novel 3D face recognition algorithm, which employs discriminatory anthropometric distances between carefully selected local facial features. This algorithm neither uses general purpose pattern recognition approaches, nor does it directly extend 2D face recognition techniques to the 3D domain. Instead, it is based on an understanding of the structurally diverse characteristics of human faces, which we isolate from the scientific discipline of facial anthropometry. We demonstrate the effectiveness and superior performance of the proposed algorithm, relative to existing benchmark 3D face recognition algorithms. A related contribution is the development of highly accurate and reliable 2D+3D algorithms for automatically detecting 10 anthropometric facial fiducial points. While developing these algorithms, we identify unique structural/textural properties associated with the facial fiducial points. Furthermore, unlike previous algorithms for detecting facial fiducial points, we systematically evaluate our algorithms against manually located facial fiducial points on a large database of images. Our third contribution is the development of an effective algorithm for computing the structural dissimilarity of 3D facial surfaces, which uses a recently developed image similarity index called the complex-wavelet structural similarity index. This algorithm is unique in that unlike existing approaches, it does not require that the facial surfaces be finely registered before they are compared. Furthermore, it is nearly an order of magnitude more accurate than existing facial surface matching based approaches. Finally, we propose a simple method to combine the two new 3D face recognition algorithms that we developed, resulting in a 3D face recognition algorithm that is competitive with the existing state-of-the-art algorithms.Electrical and Computer Engineerin

Stochastic modeling of seafloor morphology

Thesis (Ph. D.)--Joint Program in Oceanography (Massachusetts Institute of Technology, Dept. of Earth, Atmospheric, and Planetary Sciences; and the Woods Hole Oceanographic Institution), June 1990."April 1990."At scale lengths less than 100 km or so, statistical descriptions of seafloor morphology can be usefully employed to characterize processes which form and reshape abyssal hills, including ridge crest volcanism, off-axis tectonics and volcanism, mass wasting, sedimentation, and post-depositional transport. The objectives of this thesis are threefold: (1) to identify stochastic parameterizations of small-scale topography that are geologically useful, (2) to implement procedures for estimating these parameters from multibeam and side-scan sonar surveys that take into account the finite precision, resolution, and sampling of real data sets, and (3) to apply these techniques to the study of marine geological problems. The seafloor is initially modeled as a stationary, zero-mean, Gaussian random field completely specified by its two-point covariance function. An anisotropic two-point covariance function is introduced that has five free parameters describing the amplitude, orientation, characteristic width and length, and Hausdorff (fractal) dimension of seafloor topography. The general forward problem is then formulated relating this model to the statistics of an ideal multibeam echo sounder, in particular the along-track auto-covariance functions of individual beams and the cross-covariance functions between beams of arbitrary separation. Using these second moments as data functionals, we then pose the inverse problem of estimating the seafloor parameters from realistic, noisy data sets with finite sampling and beamwidth, and we solve this inverse problem by an iterative, linearized, least squares method. Resolution of this algorithm is tested against ship variables such as length of data, the orientation of ship track with respect to topographic grain, and the beamwidth. This analysis is conducted by inverting sets of synthetic data with known statistics. The mean and standard deviation of the inverted parameters can be directly compared with the input parameters and the standard errors output from the inversion. The experiments conducted in this study show that the rms seafloor height can be estimated to within -15% and anisotropic orientation to within ~5* (for a strong lineation) using very short track lengths (down to 3 characteristic lengths, or -10 to 100 km), and characteristic lengths of seafloor topography can be estimated to within -25% using fairly short track lengths (down to 5 or 6 characteristic lengths, or 10's of km to -200 kin). The number of characteristic lengths sampled by a ship track, and hence the accuracy of the estimation, is maximized when the ship track runs perpendicular to abyssal hill lineation. Using the assumed beamwidth, the measured noise values, and the seafloor parameters recovered from the inversion, Sea Beam "synthetics" are generated whose statistical character can be directly compared with raw Sea Beam data. However, these comparisons are spatially limited in the athwart ship direction. A recent SeaMARC II survey along the flanks and crest of the East Pacific Rise between 130 and 15* N included sufficient off-axis topography to permit a comparison of a complete 2-D synthetic topographic field with a region of abyssal-hill terrain that has close to 100% data coverage. Synthetic data is compared to both Sea Beam swaths and SeaMARC II survey data. These comparisons generally indicate that we are successful in characterizing the second order properties of the seafloor. They also indicate the directions we will need to take to improve our modeling, including generalization of the second-order model and characterization of higher moments. The inversion procedure is applied to a data set of 64 near-ridge Sea Beam swaths to characterize near ridge abyssal hill morphology and its relationship to ridge properties. Much of the data (27 swaths) comes from cruises to the Pacific-Cocos spreading section of the East Pacific Rise between 9* and 15* N. These data provide very good abyssal hill coverage of this well-mapped and studied ridge section and form the basis of a regional analysis of the correlation between ridge morphology and stochastic abyssal hill parameters. This regional analysis suggests a strong relationship between magma supply and the character of abyssal hills. We also have data from near the Rivera (9) and Nazca (7) spreading sections of the East Pacific Rise, the Mid-Atlantic Ridge (18), and the Indian- African Ridge (3). Though spotty, this constitutes a good initial data set for the analysis of correlations among covariance parameters and between parameters and ridge characteristics, especially spreading rate. A working hypothesis is introduced to explain the observations within a geological framework. This hypothesis contends 1) that the maximum size of abyssal hills is related to the lithosphere's ability to elastically support the load, 2) that fissuring and horst and graben formation dominate abyssal hill formation at fast spreading ridges, and 3) that volcanic edifice formation, modified by faulting driven by lithospheric necking, dominates abyssal hill formation at slow spreading ridges. To quantify abyssal hill characteristics such as vertical and lateral asymmetry and "peakiness" we must appeal to higher statistical moments than order two. A mathematical framework is introduced for the study of higher moments of a topographic field. This framework is built upon the concept that lower-order moment provide the groundwork for studying the higher-order moments. A simple 1-D parameterized model is proposed for moments up to order 4. This model includes two parameters for the third moment, describing vertical and lateral asymmetries, and one for the fourth moment, which describes the peakiness of topography. Initial methods are developed for estimating these parameters from bathymetric profiles. Results from the near ridge data set are presented and interpreted with regard to abyssal hill forming processes.by John Anson Goff.Ph.D