An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi

Genetics and genomics of aortic form and function

The thoracic aorta is a dynamic organ which adapts and remodels throughout life. Thoracic aortic size, shape and function are important contributors to both cardiovascular health and disease and risk of aortic disease. A complex interaction of environmental, genetic and haemodynamic factors is mediated by cells of the aortic wall. This thesis presents aortic phenotyping, genotyping and genome-wide associations of aortic traits in a large healthy cohort of 1218 volunteers. This is the largest study to report normal parameters for healthy thoracic aortic size, shape and function derived from cardiovascular magnetic resonance imaging. Anthropometric and cardiovascular risk factors such as age, gender, body fat mass and lipid profile are identified as significant determinants of aortic phenotype. The work suggests that cardiovascular risk factors could impair normal adaptive aortic remodelling with age. Genome-wide association studies of aortic dimensions and function identify new common variants, genes and pathways which could be important in aortic biology and cardiovascular risk. These include genes involved in cardiovascular development (eg PCDH7 and SON associated with aortic root diameter), autonomic cardiovascular responses (eg GABA receptor genes associated with aortic root diameter), fibrosis (eg ACTC1, AGTR1 associated with ascending aortic distensibility, BAMBI and MYOD associated with descending aortic distensibility) and obesity (eg ARID5B and IRX3 associated with aortic pulse wave velocity and ascending aortic area respectively). Multiple regulatory pathways including TGF-ß and IGF signalling (IGF1R, IGF2R), are identified which are associated with aortic dimensions and function. Joint trait analysis of aortic root dimensions identifies a new genome-wide significant association with TENM4, a key driver of early mesodermal development, and suggestive association with PTN, which is functionally related and plays a key role in angiogenesis. The primary analyses are complemented by exploratory assessment of rare genetic variation in bicuspid aortic valve (BAV) using panel sequencing in 177 patients. Rare variants might cause, or modify phenotype in BAV, but the clinical utility of panel sequencing remains poor. A further complementary study investigates the interaction of haemodynamics with aortic cellular phenotype, using microarray assessment of aortic endothelial cell transcriptomic response to shear stress pattern. Several genes of interest in atherosclerosis and aortic disease are differentially expressed with shear stress pattern, such as FABP4, ANGPT2, FILIP1, KIT, DCHS1, TGFBR3 and LOX. This work yields new insights into aortic phenotype, identifies key loci which might determine aortic traits and explores the complex interdependence of genetics, haemodynamics and environmental variables in aortic biology.Open Acces

Togo's Political and Socio-Economic Development (2019-2021) (author's extended and annotated version of BTI 2022 - Togo Country Report)

The Gnassingbé clan has ruled the country since 1967. The demand for political alternance, constituted the major contentious issue between the government and the challengers of the Gnassingbé regime throughout the survey period. The first local elections since more than 30 years took finally place on 30 June 2019 and resulted in the victory of the ruling party. Shortly afterwards, in February 2020, the President won also the disputed presidential elections and thus consolidated his power, assisted by the loyal army and security services. The outbreak of the Corona epidemic in Togo in April 2020 and the subsequent economic recession may have contributed to limit popular protest against the Gnassingbé regime. The human rights record of the government has improved but remains poor. Despite undeniable improvements to the framework and appearance of the regime's key institutions during the review period, democracy remains far from complete. However, the international community, notably Togo’s African peers, the AU and ECOWAS, followed a 'laissez-faire' approach in the interests of regional stability and their national interests in dealing with Togo. Economic growth remained stable at about 5% per annum (before Corona). Public investment in infrastructure and increases in agricultural productivity, notably of export crops, had been the key drivers of economic growth. However, growth remains vulnerable to external shocks and the climate and has not been inclusive. Moreover, it was overshadowed by increasing inter-personal and regional inequality as well as an increase in extreme poverty. Money-laundering, illegal money transfers and trafficking grew alarmingly. Nevertheless, the business climate improved considerably

Privaatsust säilitava raalnägemise meetodi arendamine kehalise aktiivsuse automaatseks jälgimiseks koolis

Väitekirja elektrooniline versioon ei sisalda publikatsiooneKuidas vaadelda inimesi ilma neid nägemata? Öeldakse, et ei ole viisakas jõllitada. Õigus privaatsusele on lausa inimõigus. Siiski on inimkäitumises palju sellist, mida teadlased tahaksid uurida inimesi vaadeldes. Näiteks tahame teada, kas lapsed hakkavad vahetunnis rohkem liikuma, kui koolis keelatakse nutitelefonid? Selle välja selgitamiseks peaks teadlane küsima lapsevanematelt nõusolekut võsukeste vaatlemiseks. Eeldusel, et lapsevanemad annavad loa, oleks klassikaliseks vaatluseks vaja tohutult palju tööjõudu – mitu vaatlejat koolimajas iga päev piisavalt pikal perioodil enne ja pärast nutitelefoni keelu kehtestamist. Doktoritööga püüdsin lahendada korraga privaatsuse probleemi ja tööjõu probleemi, asendades inimvaatleja tehisaruga. Kaasaegsed masinõppe meetodid võimaldavad luua mudeleid, mis tuvastavad automaatselt pildil või videos kujutatud objekte ja nende omadusi. Kui tahame tehisaru, mis tunneb pildil ära inimese, tuleb moodustada masinõppe andmestik, kus on pilte inimestest ja pilte ilma inimesteta. Kui tahame tehisaru, mis eristaks videos madalat ja kõrget kehalist aktiivsust, on vaja vastavat videoandmestikku. Doktoritöös kogusingi andmestiku, kus video laste liikumisest on sünkroniseeritud puusal kantavate aktseleromeetritega, et treenida mudel, mis eristaks videopikslites madalamat ja kõrgemat liikumise intensiivsust. Koostöös Tehonoloogiainstituudi iCV laboriga arendasime välja videoanalüüsi sensori prototüübi, mis suudab reaalaja kiirusel hinnata kaamera vaateväljas olevate inimeste kehalise aktiivsuse taset. Just see, et tehisaru suudab tuletada videost kehalise aktiivsuse informatsiooni ilma neid videokaadreid salvestamata ega inimestele üldsegi näitamata, võimaldab vaadelda inimesi ilma neid nägemata. Väljatöötatud meetod on mõeldud kehalise aktiivsuse mõõtmiseks koolipõhistes teadusuuringutes ning seetõttu on arenduses rõhutatud privaatsuse kaitsmist ja teaduseetikat. Laiemalt vaadates illustreerib doktoritöö aga raalnägemistehnoloogiate potentsiaali töötlemaks visuaalset infot linnaruumis ja töökohtadel ning mitte ainult kehalise aktiivsuse mõõtmiseks kõrgete teaduseetika kriteerimitega. Siin ongi koht avalikuks aruteluks – millistel tingimustel või kas üldse on OK, kui sind jõllitab robot?  How to observe people without seeing them? They say it's not polite to stare. The right to privacy is considered a human right. However, there is much in human behavior that scientists would like to study via observation. For example, we want to know whether children will start moving more during recess if smartphones are banned at school? To figure this out, scientists would have to ask parental consent to carry out the observation. Assuming parents grant permission, a huge amount of labour would be needed for classical observation - several observers in the schoolhouse every day for a sufficiently long period before and after the smartphone ban. With my doctoral thesis, I tried to solve both the problem of privacy and of labor by replacing the human observer with artificial intelligence (AI). Modern machine learning methods allow training models that automatically detect objects and their properties in images or video. If we want an AI that recognizes people in images, we need to form a machine learning dataset with pictures of people and pictures without people. If we want an AI that differentiates between low and high physical activity in video, we need a corresponding video dataset. In my doctoral thesis, I collected a dataset where video of children's movement is synchronized with hip-worn accelerometers to train a model that could differentiate between lower and higher levels of physical activity in video. In collaboration with the ICV lab at the Institute of Technology, we developed a prototype video analysis sensor that can estimate the level of physical activity of people in the camera's field of view at real-time speed. The fact that AI can derive information about physical activity from the video without recording the footage or showing it to anyone at all, makes it possible to observe without seeing. The method is designed for measuring physical activity in school-based research and therefore highly prioritizes privacy protection and research ethics. But more broadly, the thesis illustrates the potential of computer vision technologies for processing visual information in urban spaces and workplaces, and not only for measuring physical activity or adhering to high ethical standards. This warrants wider public discussion – under what conditions or whether at all is it OK to have a robot staring at you?https://www.ester.ee/record=b555972