This paper deals with identification of persons using retinal biometry. The retinal vasculature is invariant and unique to everyone, which determines it for biometric purposes. The first part of the work includes information about biometrics, biometric systems and reliability measures. The next part describes the principle of using experimental video ophthalmoscope, which was used for retinal vascular imaging and includes the literature research of use of retinal images for biometrics, feature extraction methods and similarity measures. Finally, two algorithms to use the input data are proposed and realized in programming environment MATLAB®. The methods are tested and evaluated on a data set from experimental video ophthalmoscope and on publicly available STRaDe and DRIVE databases.

V posledním desetiletí došlo ke zvýšení zájmu o užití 3D dat k biometrické identifikaci osob. Možná vůbec největší výzkum proběhl v oblasti 3D rozpoznávání podle obličeje, přičemž je v současné době dostupných vícero komerčních zařízení. V oblastni rozpoznávání podle 3D geometrie ruky byl v minulých letech proveden určitý výzkum jehož výsledkem však nebylo žádné komerční zařízení. Nezávisle na tomto výzkumu se v posledních letech velmi rozšířil trh s cenově dostupnými 3D sensory, což potenciálně umožňuje jejich nasazení v mnoha typech biometrických systémů. Hlavním cílem této práce je vytvořit funkční vzorek bezdotykového systému pro rozpoznávání osob podle 3D geometrie ruky, který bude používat novou levnou kameru RealSense 3D vyvíjenou v současné době firmou Intel. Jedním z problémů při použití RealSense kamery je její velmi malý form factor, který je příčinou nižší kvality výsledných snímků v porovnání s velmi drahými alternativami, které byly použity v již dříve zmíněném výzkumu 3D biometrických systémů. Práce se snaží analyzovat robustnost různých 2D a 3D příznaků a vyzkoušet několik různých přístupů k jejich fúzi. Rovněž je vyhodnocena výkonnost výsledného systému, kde je ukázáno, že navržené řešení dosahuje výsledků porovnatelných se state-of-the-art.

This thesis attempts to find an alternative method for biometric identification using retinal images. First part is focused on the introduction to biometrics, human eye anatomy and methods used for retinal biometry. The essence of neural networks and deep learning methods is described as it will be used practically. In the last part of the thesis a chosen identification algorithm and its implementation is described and the results are presented.

This thesis deals with the design and implementation of a mobile application for BUT employees. The final application is written for Android and iOS in React Native. The goal is to facilitate the daily activities of an employee, which leads to more efficient work and reduced administration. The theoretical part deals with the selection of the most important modules in the information system based on statistical data and questionnaires. The implementation part contains the design of the user interface of selected modules and a description of the application implementation. The final application was tested by a group of employees and received a very good verbal evaluation.

The aim of this semester project is to design a suitable database structure for the audio-visual data, parts extracted from the data corresponding to secondary biometric and non-biometric characteristics and description of these characters. The resulting database will be used for research into the identification of audio-visual content of multimedia data.

The thesis deals with design and implementation of a tool for simulating marks of chosen skin diseases into a synthetic fingerprint. The diseases selected to work with are warts and atopic eczema. The marks of diseases are generated into a synthetic fingerprint image created by the SFinGe application. Existing disesase-affected fingerprints from the STRaDe database are analysed in detail. Then, methods for simulating the diseases into a synthetic fingerprint are proposed, implemented, and the results are evaluated.

Background: Cross-sectional survey of ocular biometry and visual function in healthy eyes across the life span of a German population aged 20 to 69 years (n = 218). Subject number in percent per age category reflected the percentage within the respective age band of the population of Leipzig, Germany. Methods: Measurements obtained: subjective and objective refraction, best-corrected visual acuity, accommodation, contrast sensitivity, topography and pachymetry with Scheimpflug camera, axial length with non-contact partial coherence interferometry, and spectral-domain optical coherence tomography of the retina. Pearson correlation coefficients with corresponding p-values were given to present interrelationships between stature, biometric and refractive parameters or their associations with age. Two-sample T-tests were used to calculate gender differences. The area under the logarithmic contrast sensitivity function (AULCSF) was calculated for the analysis of contrast sensitivity as a single figure across a range of spatial frequencies. Results: The results of axial length (AL), anterior chamber depth (ACD) and anterior chamber volume (ACV) differed as a function of the age of the participants (rho (p value): AL −0.19 (0.006), ACD −0.56 (< 0.001), ACV-0.52 (< 0.001)). Longer eyes had deeper ACD (AL:ACD 0.62 (< 0.001), greater ACV (AL:ACV 0.65 (< 0.001) and steeper corneal radii (AL:R1ant; R2ant; R1post; R2post 0.40; 0.35; 0.36; 0.36 (all with (< 0.001)). Spherical equivalent was associated with age (towards hyperopia: 0.34 (< 0.001)), AL (−0.66 (< 0.001)), ACD (−0.52 (< 0.001)) and ACV (−0.46 (< 0.001)). Accommodation was found lower for older subjects (negative association with age, r = −0.82 (< 0.001)) and contrast sensitivity presented with smaller values for older ages (AULCSF −0.38, (< 0.001)), no change of retinal thickness with age. 58 % of the study cohort presented with a change of refractive correction above ±0.50 D in one or both eyes (64 % of these were habitual spectacle wearers), need for improvement was present in the young age-group and for older subjects with increasing age. Conclusion: Biometrical data of healthy German eyes, stratified by age, gender and refractive status, enabled cross-comparison of all parameters, providing an important reference database for future patient-based research and specific in-depth investigations of biometric data in epidemiological research. Trial registration: ClinicalTrials.gov # NCT01173614 July 28, 2010

Alzheimer's Disease (AD) affects nearly 5.4 million Americans as of 2016 and is the most common form of dementia. The disease is characterized by the presence of neurofibrillary tangles and amyloid plaques [1]. The amount of plaques are measured by Braak stage, post-mortem. It is known that AD is positively associated with hypercholesterolemia [16]. As statins are the most widely used cholesterol-lowering drug, there may be associations between statin use and AD. We hypothesize that those who use statins, specifically lipophilic statins, are more likely to have a low Braak stage in post-mortem analysis. In order to address this hypothesis, we wished to fit a regression model for ordinal outcomes (e.g., high, moderate, or low Braak stage) using data collected from the National Alzheimer's Coordinating Center (NACC) autopsy cohort. As the outcomes were matched on the length of follow-up, a conditional likelihood-based method is often used to estimate the regression coefficients. However, it can be challenging to solve the conditional-likelihood based estimating equation numerically, especially when there are many matching strata. Given that the likelihood of a conditional logistic regression model is equivalent to the partial likelihood from a stratified Cox proportional hazard model, the existing R function for a Cox model, coxph( ), can be used for estimation of a conditional logistic regression model. We would like to investigate whether this strategy could be extended to a regression model for ordinal outcomes. More specifically, our aims are to (1) demonstrate the equivalence between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the likelihood of a conditional logistic regression model, (2) prove equivalence, or lack there-of, between the exact partial likelihood of a stratified discrete time Cox proportional hazards model and the conditional likelihood of models appropriate for multiple ordinal outcomes: an adjacent categories model, a continuation-ratio model, and a cumulative logit model, and (3) clarify how to set up stratified discrete time Cox proportional hazards model for multiple ordinal outcomes with matching using the existing coxph( ) R function and interpret the regression coefficient estimates that result. We verified this theoretical proof through simulation studies. We simulated data from the three models of interest: an adjacent categories model, a continuation-ratio model, and a cumulative logit model. We fit a Cox model using the existing coxph( ) R function to the simulated data produced by each model. We then compared the coefficient estimates obtained. Lastly, we fit a Cox model to the NACC dataset. We used Braak stage as the outcome variables, having three ordinal categories. We included predictors for age at death, sex, genotype, education, comorbidities, number of days having taken lipophilic statins, number of days having taken hydrophilic statins, and time to death. We matched cases to controls on the length of follow up. We have discussed all findings and their implications in detail.

A large database of domestic mammal bone measurements from sites across Greenland, Iceland, the Faroe Islands, and the Northern and Western Isles of Scotland is presented. The reasons for variations in bone growth of domestic ungulates are examined in detail; nutrition is identified as a key factor in the determination of adult bone size and shape. Possible sources of variation in bone size in both time and space in the North Atlantic region are identified. Four hypotheses are proposed; firstly that bone dimensions, particularly breadth, will decrease with increasing latitude in the study region; secondly that higher status sites will raise larger livestock than lower status sites within the same time period and region; thirdly the size of domestic mammals in the Northern and Western Isles of Scotland will increase in the Later Iron Age, possibly in relation to increased fodder supply; finally at times of environmental degradation (climatic and/or landscape) domestic mammal size will decrease. The latitude hypothesis could only be partly upheld; there is no evidence for increased size with site status; a small increase in size is noted at some Scottish Iron Age sites and varying results are found for the environmental degradation hypothesis. The results are discussed with particular reference to how changes in the skeletal proportions of domestic mammals affect their human carers and beneficiaries. The potential of further expanding the dataset and integrating biometrical data with other forms of evidence to create a powerful tool for the examination of economic and environmental changes at archaeological sites is discussed. / The Division of AGES (University of Bradford), the Andy Jagger Fund (University of Bradford), the Francis Raymond Hudson Fund (University of Bradford), the Viking Society, the Prehistoric Society, SYNTHESIS and the Paddy Coker Research Fund (Biogeographical Society)

The overarching goal of this dissertation is to integrate heterogeneous data for the estimation of disease coheritability and subtyping. Chapter 2 focuses on the significance and estimation of heritability and coheritability, which quantify the proportion of phenotypic variation attributable to genetic factors and the genetic correlations between different traits, respectively. To achieve this, we develop robust statistical methods based on estimating equations that account for familial correlations and the computational challenges posed by large pedigrees and extensive datasets. Our methods are evaluated through simulations, demonstrating satisfactory consistency and robust inference properties. Compared to simpler methods performing separate trait analysis, our approaches show a greater power through joint analysis of multiple traits. An application to the analysis of heritability and coheritability in electronic health record (EHR) data reveals substantial genetic correlations between mental disorders and metabolic/endocrine measurements, suggesting shared genetic influences that warrant further investigation. These findings have implications for understanding these conditions' etiology, diagnosis, and treatment. Chapters 3 and 4 focus on the importance of patient subtyping for personalized mental health care, particularly relevant to the substantial variability observed in mental disorders. Chapter 3 develops methods for subtyping patients with mental disorders using various data modalities and variational inference. We propose latent mixture models inspired by the Item Response Theory to handle both binary and continuous data. We also introduce Black Box Variational Inference (BBVI) algorithms to overcome the challenges of numeric integration in nonlinear models. Our numerical experiments validate the proposed methods, demonstrating that variance-controlling techniques improve convergence speed and reduce iteration variance. However, the proposed algorithm encounters limitations with latent mixture models containing binary modalities due to approximations used in non-conjugate posterior distributions resulting from the non-exponential family likelihood function. Chapter 4 investigates multi-modal integration techniques for subtyping patients using data from the Adolescent Brain Cognitive Development (ABCD) study. We introduce a Bayesian hierarchical joint model with latent variables and utilize Pólya-Gamma augmentation for posterior approximation, which enables efficient Gibbs sampling and accurate estimation of model parameters. Extensive simulations confirm the consistency of estimators and the prediction accuracy of our method. Applying these methods to patient clustering in the ABCD study provides information for identifying potential clinical subtypes within mental health, which can inform the development of targeted psychological and educational interventions, ultimately improving mental health outcomes. Keywords: latent mixture model, integrative analysis, coheritability, multi-modality, disease subtyping, variational inference, Pólya-Gamma