The collection of Victorian-themed wearables and accessories of  "No Person Detected" serves as an innovative solution to the issues surrounding biometric technology and the invasion of privacy. This wearable technology was designed to counteract the involuntary recording of an individual's unique biometric data through the use of body cameras and CCTV, which can be accessed by law enforcement and marketing companies. The technology represents a democratization of design ideas and collaboration that allows individuals to create adversarial fashion and provides a level of biometric protection. This thesis explores the potential of technological innovation and collaboration to result in a more privacy-conscious society, one where individuals can take control of their personal data and protect themselves against the dangers of biometric tracking. The convergence of fashion, technology, and design has the potential to revolutionize how we approach privacy in a digital age, and "No Person Detected" represents an exciting step towards that future. / Master of Fine Arts / As technology becomes a larger component of our daily lives, our digital footprint continues to expand, leaving behind sensitive identifying information. From this data, law enforcement agencies such as the FBI and ICE derive insights and conclusions about our lives. Due to unreliable data, facial recognition technology (FRT) has demonstrated implicit bias, particularly toward racialized bodies. This highlights the need for public education and responsible online behavior and raises questions about the privacy and security of personal data. At the intersection of fashion, history, and technology, "No Person Detected" aims to fight against the involuntary collection of biometric data in an adversarial way. With the proliferation of FRT and the accumulation of personal data from a variety of sources, it is crucial that both businesses and individuals establish transparent policies to protect user data. This thesis highlights both the historical context of racism in policing and the significance of privacy in the digital age.

Interest in facial recognition has increased rapidly during the past decade. Increased computational power and huge amounts of available data have made facial recognition both possible and useful. Bio-metrical identification is one of the common applications for facial recognition.The population in Sweden is aging. Moreover, many people remain living on their own until old age. This introduces new challenges to society. How do we maintain the autonomy of elderly, and support their well-being despite of the challenges introduced by aging?This thesis presents a study of the potential of facial recognition in elderly care. In the thesis work a need for facial recognition system in elderly care is identified, a system architecture to meet the need is presented, the implementation process of such system’s prototype is described, and the feasibility of the prototype is evaluated.One of the results of the study indicates that there is a need in elderly care to help seniors to verify and identify caregivers who visit them. The study shows that a facial recognition system which presents information about the visiting caregiver to the elderly would support them in their daily life. The user interface of the developed prototype is feasible, but as it is now, the facial recognition part of the program is not accurate enough to be used in a real life context. Ways of improving the facial recognition functionality of such a system should be studied in future research. / Intresset för ansiktsigenkänning har ökat snabbt under det senaste decenniet. Detta har gjort ansiktsigenkänning både möjlig och användbar. Biometri och identifiering är vanliga användningssätt för ansiktsigenkänning.Sverige befolkning åldras. De äldre fortsätter dessutom att i hög grad bo ensamma. Detta introducerar nya utmaningar för samhället. Hur kan vi bibehålla de äldres autonomi and stötta deras välmående, trots ålderns krämpor?Denna uppsats presenterar en studie om potentialen för att använda ansiktsigenkänning inom äldrevården. I arbetet identifieras behovet av ett ansiktsigenkänningssystem inom äldrevården, en systemarkitektur för att tillgodose detta behov presenteras, implementeringsprocessen av en prototyp av ett sådant system beskrivs samt genomförbarheten av ett sådant system utvärderas. Ett av studiens resultat indikerar att det finns ett behov inom äldreomsorgen att hjälpa seniorer att identifiera och verifiera den personal som besöker dem. Studien visar att ett ansiktsigenkänningssystem som visar information om besökande personal till seniorerna skulle kunna hjälpa dem i deras dagliga liv.Användargränssnittet i den utvecklade prototypen är användbar, men i dess nuvarande stadie är ansiktsigenkänningsdelen av programmet inte exakt nog för att kunna användas i verkligheten. Metoder för att förbättra ansiktsigenkänningsfunktionen i ett sådant system är ett uppslag för framtida forskning.

Identifying individuals in historical photographs is important for preserving material culture, correcting historical records, and adding economic value. Historians, antiques dealers, and collectors often rely on manual, time-consuming approaches. While Artificial Intelligence (AI) offers potential solutions, it's not widely adopted due to a lack of specialized tools and inherent inaccuracies and biases. In my dissertation, I address this gap by combining the complementary strengths of human intelligence and AI. I introduce Photo Sleuth, a novel person identification pipeline that combines crowdsourced expertise with facial recognition, supporting users in identifying unknown portraits from the American Civil War era (1861--65). Despite successfully identifying numerous unknown photos, users often face the `last-mile problem' --- selecting the correct match(es) from a shortlist of high-confidence facial recognition candidates while avoiding false positives. To assist experts, I developed Second Opinion, an online tool that employs a novel crowdsourcing workflow, inspired by cognitive psychology, effectively filtering out up to 75% of facial recognition's false positives. Yet, as AI models continually evolve, changes in the underlying model can potentially impact user experience in such crowd--expert--AI workflows. I conducted an online study to understand user perceptions of changes in facial recognition models, especially in the context of historical person identification. Our findings showed that while human-AI collaborations were effective in identifying photos, they also introduced false positives. To reduce these misidentifications, I built Photo Steward, an information stewardship architecture that employs a deliberative workflow for validating historical photo identifications. Building on this foundation, I introduced DoubleCheck, a quality assessment framework that combines community stewardship and comprehensive provenance information, for helping users accurately assess photo identification quality. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that foster accurate decision-making in the context of historical photo identification. / Doctor of Philosophy / Identifying historical photos offers significant cultural and economic value; however, the identification process can be complex and challenging due to factors like poor source material and limited research resources. In my dissertation, I address this problem by leveraging the complementary strengths of human intelligence and Artificial Intelligence (AI). I built Photo Sleuth, an online platform, that helps users in identifying unknown portraits from the American Civil War era. This platform employs a novel person identification workflow that combines crowdsourced human expertise and facial recognition. While AI-based facial recognition is effective at quickly scanning thousands of photos, it can sometimes present challenges. Specifically, it provides the human expert with a shortlist of highly similar-looking candidates from which the expert must discern the correct matches; I call this as the `last-mile problem' of person identification. To assist experts in navigating this challenge, I developed Second Opinion, a tool that employs a novel crowdsourcing workflow inspired by cognitive psychology, named seed-gather-analyze. Further, I conducted an online study to understand the influence of changes in the underlying facial recognition models on the downstream person identification tasks. While these tools enabled numerous successful identifications, they also occasionally led to misidentifications. To address this issue, I introduced Photo Steward, an information stewardship architecture that encourages deliberative decision-making while identifying photos. Building upon the principles of information stewardship and provenance, I then developed DoubleCheck, a quality assessment framework that presents pertinent information, aiding users in accurately evaluating the quality of historical photo IDs. Through my dissertation, I explore the design and deployment of human-AI collaborative tools, emphasizing the creation of sustainable online communities and workflows that encourage accurate decision-making in the context of historical photo identification.

The concept and practice of surveillance has long existed in our society, yet with the development of technology, it has taken on new forms and capabilities. As a result, surveillance technology has become integrated in our society, influencing norms and shaping imaginaries surrounding it. While many existing studies have thoroughly examined people's experiences with surveillance technologies, there has been little attention paid to the efforts of advocacy groups in challenging and reshaping the mainstream imaginaries regarding surveillance technology. Using narrative analysis, this thesis aims to address this gap and explore the sociotechnical imaginaries surrounding facial recognition technology of four advocacy groups: a) Fight for the Future, b) Big Brother Watch, c) Electronic Frontier Foundation, d) Surveillance Technology Oversight Project. This study uncovers that these groups' shared sociotechnical imaginary aligns closely with modern liberal ideals, highlighting the possibility of separating public and private life, the necessity for not only moderate government intervention, but healthy commercial competitions, as well as public education. In other words, I argue that resisting against a particular technology and its associated power dynamics does not always represent a challenge to the fundamental power structure.

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine which method has the highest accuracy of prediction rate. The three methods are reviewed and then compared via experiments. OpenCV, CMake, and Visual Studios were used as tools to conduct experiments. Analysis were conducted to identify which method has the highest accuracy of prediction rate with various experimental factors. By feeding a number of sample images of different people which serve as experimental subjects. The machine is first trained to generate features for each person among the testing subjects. Then, a new image was tested against the “learned” data and be labeled as one of the subjects. With experimental data analysis, the Eigenfaces method was determined to have the highest prediction rate of the three algorithms tested. The Local Binary Pattern Histogram (LBP) was found to have the lowest prediction rate. Finally, LBP was selected for the algorithm improvement. In this project, LBP was improved by identifying the most significant regions of the histograms for each person in training time. The weights of each region are assigned depending on the gray scale contrast. At recognition time, given a new face, different weights are assigned to different regions to increase prediction rate and also speed up the real time recognition. The experimental results confirmed the performance improvement.

Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2011-08-05Bitstream added on 2014-06-13T19:38:57Z : No. of bitstreams: 1 kami_gjc_me_sjrp.pdf: 1342570 bytes, checksum: 240c6d6b92fda1861dfbed94c9213a10 (MD5) / As técnicas para identificação biométrica têm evoluído cada vez mais devido à necessidade que os seres humanos têm de identificar as pessoas em tempo real e de forma precisa para permitir o acesso a determinados recursos, como por exemplo, as aplicações e serviços WEB. O reconhecimento facial é uma técnica biométrica que apresenta várias vantagens em relação às demais, tais como: uso de equipamentos simples e baratos para a obtenção das amostras e a possibilidade de se realizar o reconhecimento em sigilo e à distância. O reconhecimento de faces a partir de vídeo é uma tendência recente na área de Biometria. Esta dissertação tem por objetivo principal comparar diferentes técnicas de reconhecimento facial a partir de vídeo para determinar as que apresentam um melhor compromisso entre tempo de processamento e precisão. Outro objetivo é a incorporação dessas melhores técnicas no sistema de autenticação biométrica em ambientes de E-Learning, proposto em um trabalho anterior. Foi comparado o classificador vizinho mais próximo usando as medidas de distância Euclidiana e Mahalanobis com os seguintes classificadores: Redes Neurais MLP e SOM, K Vizinhos mais Próximos, Classificador Bayesiano, Máquinas de Vetores de Suporte (SVM) e Floresta de Caminhos Ótimos (OPF). Também foi avaliada a técnica de Modelos Ocultos de Markov (HMM). Nos experimentos realizados com a base Recogna Video Database, criada especialmente para uso neste trabalho, e Honda/UCSD Video Database, os classificadores apresentaram os melhores resultados em termos de precisão, com destaque para o classificador SVM da biblioteca SVM Torch. A técnica HMM, que incorpora informações temporais, apresentou resultados melhores do que as funções de distância, em termos de precisão, mas inferiores aos classificadores / The biometric identification techniques have evolved increasingly due to the need that humans have to identify people in real time to allow access to certain resources, such as applications and Web services. Facial recognition is a biometric technique that has several advantages over others. Some of these advantages are the use of simple and cheap equipment to obtain the samples and the ability to perform the recognition in covert mode. The face recognition from video is a recent approach in the area of Biometrics. The work in this dissertation aims at comparing different techniques for face recognition from video in order to find the best rates on processing time and accuracy. Another goal is the incorporation of these techniques in the biometric authentication system for E-Learning environments, proposed in an earlier work. We have compared the nearest neighbor classifier using the Euclidean and Mahalanobis distance measures with some other classifiers, such as neural networks (MLP and SOM), k-nearest neighbor, Bayesian classifier, Support Vector Machines (SVM), and Optimum Path Forest (OPF). We have also evaluated the Hidden Markov Model (HMM) approach, as a way of using the temporal information. In the experiments with Recogna Video Database, created especially for this study, and Honda/UCSD Video Database, the classifiers obtained the best accuracy, especially the SVM classifier from the SVM Torch library. HMM, which takes into account temporal information, presented better performance than the distance metrics, but worse than the classifiers

Person identification has in the recent years gained notoriety as one of the most powerful ways of extracting information from image data. This thesis is focused on the task of human identification from facial photographs. To solve this task, we employ algorithms based on neural networks, which produce more robust results than traditional algorithms. In this thesis, we studied the common approaches for solving this problem and based on the gathered knowledge we created an architecture of a neural network trained to tackle the task of human identification and verification based on facial photographs. We have then further improved the model architecture and the training process by performing various experiments and observing the results. The final model has reached an accuracy comparable to other state-of-the-art models. Furthermore, we created a desktop application to demonstrate the results visually and to enable easier manipulation with the identity database. The knowledge gathered in this thesis can be used for improvements of current identification models or models modified for solving similar tasks.

Bakgrund Biometriteknik är paraplybegrepp för olika automatiserade tekniker som används vid identifikation av individer. Biometriska identifikationsmetoder såsom fingeravtrycksläsning och ansiktsigenkänning har sedan länge varit reserverade för specifika syften. Idag är situationen förändrad, biometriteknik finns tillgänglig för allmänheten och används i allt större utsträckning. Biometriska metoder för identifikation kan erbjuda säkrare identifiering gentemot vanliga lösenord. Detta är av stor vikt då privatpersoner lagrar allt mer känslig information på sina mobiltelefoner, surfplattor och datorer.  Syfte   Syftet med den här studien är att undersöka hur stor inverkan faktorerna användbarhet, säkerhet och integritet har på privatpersoners användning av biometriska metoder för identifiering på mobiltelefoner, surfplattor och datorer. Efter granskning av tidigare forskning gavs dessa tre faktorer extra fokus. Den första faktorn, säkerhet, identifierades i och med att biometriteknik erbjuder förbättrad säkerhet om tekniken används korrekt. Samt att tidigare forskning pekade på säkerhet som en aspekt privatpersoner må ha i åtanke. Den andra faktorn, användbarhet, återfinns i att forskning visar på att individer värderar användbarhet och enkelhet högt. En tredje faktor, integritet, valdes i och med att viss forsking här hade nått motsägande resultat  Metod Rapporten genomfördes främst med hjälp av en kvantitativ enkätundersökning, svar (n=121) från denna undersökning användes senare för att besvara och reflektera kring forskningsfrågan: Hur stor inverkan har faktorerna användbarhet, säkerhet och integritet på privatpersoners användning av biometriteknik? Ett kapitel som namngavs forskningsöversikt inkluderas också, här har litteratur sållats kvalitativt för att finna relevanta artiklar.  Resultat Den enkätundersökning som genomfördes påvisade att en majoritet av privatpersoner använder sig av biometriska identifieringsmetoder på sina mobiltelefoner, surfplattor samt datorer. Vidare visade det sig att faktorn användbarhet värderades högt, vissa fysiologiska egenskaper som används vid biometrisk identifiering ansågs även som mer eller mindre accepterade. Svar som gavs av respondenter visade också på att privatpersoner ej resonerar speciellt mycket kring integritet och personliga data som lagras vid användning av biometriteknik.  Slutsats Efter genomförd diskussion angående tidigare forskning och de resultat som nåddes utifrån enkätundersökningen konstaterades det att faktorn användbarhet hade stor inverkan på privatpersoners användning av biometriteknik. Säkerhet tas även i åtanke av en mindre del. Faktorn integritet ges ingen eller väldigt liten uppmärksamhet gällande användning av biometriska identifieringsmetoder hos privatpersoner. / Background Biometrics is a field including different automated technologies used for thepurpose of identify individuals. Biometric identification methods such asfingerprint scanning and facial recognition used to be a field reserved forspecific application purposes. Nowadays biometrics are used more frequently,and it is available for the public to use in their everyday life. Biometrics canprovide more secure solutions compared to normal passwords, but to achievethis adoption of said methods is key, especially seeing as users store moresensitive and personal data on their smart devices compared to yesterday’s nonsmartphones. Purpose The purpose of this study is to explore how the factors usability, security andprivacy affect people’s use of biometric solutions on their smartphones,tablets, and computers. After thoroughly reviewing previous literature thesethree main factors gained extra focus. The first factor, security, biometrics dooffer greater security advantages when used the right way. Also, previousresearch points towards security as an aspect people should have in mindwhile using biometrics. The second factor, usability, this aspect is based onprevious research showing that people tend to value functions and features thatare usable. A third factor, privacy, was chosen because research showedconflicting results regarding the importance of this factor.  Method This study was conducted primarily through a quantitative survey, answers(n=121) from this survey was later used to reflect upon the research question:How much of an impact does the factors usability, security and privacy haveover peoples use of biometrics? A chapter providing an overview of previousresearch is also included, for this chapter literature has been reviewed in aqualitative matter to sort out relevant research articles.  Results Results from the survey that was conducted showed that most individuals doindeed use biometrics on their mobile phones, tablets, and computers. Datacollected also indicates that the factor usability had a big impact on peoples useof biometrics. People also responded that they saw some physiological traitsused for biometrics as more, or less accepted to be stored and collected. Otheranswers to the survey showed that individuals generally do not care too muchabout their privacy when using biometrics. Conclusion After concluded discussions regarding previous research and the resultscollected from individuals through the survey a conclusion is reached. Thefactor usability had a big impact on individuals use biometrics. A small part ofindividuals does also consider security to be important. However, the factorprivacy was mostly ignored when it comes to using biometrics on smartphones,tablets and computers.

Facial Recognition Systems är något som har blivit populärt de senaste åren, speciellt efter den 11 september 2001. Möjligheten att kunna över-vaka personer som rör sig i olika miljöer har varit av intresse för bland annat regeringar, till exempel USA:s regering. Eftersom det finns mängder med olika typer av undersökningar och alla försöker göra så bra matchningar som möjligt av personer mot databaser, fast de utförs på olika sätt, är det intressant att se om det finns någon parameter som har en större påverkan på resultaten, oavsett om undersökningen görs med 2D, 3D eller en kombination av metoderna. Det finns många olika faktorer och parametrar som påverkar matchningsprocenten därför skall denna littera-turstudie försöka lokalisera och se om det finns någon parameter som har en större påverkan på matchningsprocenten. Det visar sig att två parametrar har en större påverkan än övriga parametrar. De är antalet bilder av varje objekt som finns att matcha i databasen och kvaliteten på indata vilket innebär kontrast och upplösning samt hur kompletta ansiktena är. / Facial Recognition Systems is something that has become popular in recent years, especially after 11 September 2001. The ability to monitor people that are moving in different environments has been of interest to particular governments, for instance the US government. Since there are a lot of different types of surveys, though performed in different ways, all trying to do the best matches of people to databases as possible, it is interesting to see if there is any parameter that has a major impact on the result. Whether the survey is done with 2D, 3D or a combination of methods, there are many different factors and parameters that affect the matching percentage. Therefore this study tries to locate and see if there is any parameter that has a greater impact on the matching percentage. It appears that two of the parameters have a greater effect on the result, than the others. These are the number of images of a test subject in the database and the quality of the input data. The quality is defined by contrast and resolution as well as how complete the faces are.

Artificial intelligence is becoming an increasingly important part of our lives and can be found in everything from fridges to phones. One of the applications of AI is the police use of facial recognition technology for law enforcement purposes. However, the use poses a major risk to fundamental rights. As part of the European Commission's initiative to create a Union fit for the digital age, the proposal for an AI Act was introduced in 2021 with the aim of setting the limit of permissible use of AI. In the act, the use of real-time facial recognition is prohibited except for a few exceptions which the police in the Union argue will severely hinder their work. However, the scope of the prohibition is ambiguous, and the act is riddled with various problems in its regulation of facial recognition used for law enforcement purposes. This thesis will therefore critically analyse the AI Act on the basis of three research questions. The first question examines how the AI Act will affect current law and the police’s ability to use facial recognition for law enforcement purposes. This thesis finds regarding real-time facial recognition, the act will replace LED as applicable law. However, for high-risk applications of FRT, there will be an interplay between the two regulations. When it comes to the effect on the police’s use of FRT for law enforcement purposes, this thesis finds that the police have no bigger reason for worry. The exceptions make a myriad of otherwise prohibited uses of real-time FRT permissible, and the use of post-FRT is not regulated in the act. The second question analyses the problems with the act and the thesis finds that there are essentially four major problems with the act. The exceptions allow for a disproportionate amount of otherwise prohibited uses of FRT, it is too difficult to interpret and the mechanisms for futureproofing are lacking. Furthermore, the interplay with Prüm II must be considered to a greater extent. The third and final question provides three different solutions to the problems. The first solution is to reduce the scope of the exceptions. The second solution is to revise the high-risk provision to make it easier to add new systems. Finally, this thesis finds that the introduction of a separate regulation for law enforcement use, like GDPR & LED, would be a good solution.