The aim of this dissertation is to study the role of attention in two important domains – news consumption and advertising. The World Economic Forum, in its Global Risks Report, has identified a “deteriorating global outlook” for the next decade. The top three contributors to this negative outlook are misinformation, climate change, and societal polarization. Specifically, the report predicts that as the technological landscape changes and polarization grows, “the truth will come under pressure” and “environmental risks could hit the point of no return.” (World Economic Forum, 2024). Therefore, the two most important imperatives facing the world today are combatting polarization through misinformation and promoting organizational social responsibility (by promoting organizations that work toward socially desirable outcomes like combatting climate change and ensuring social equity). This dissertation addresses both these issues through the lens of attention.Across 4 studies, this dissertation shows that while increased attention does help in spotting individual false claims, increasing consumers’ attention to news stories may not be a silver-bullet solution to combatting fake news narratives in longer-than-headline contexts. When people consume news stories, their impression of the story as a whole is an important determinant of how they perceive claims within that story and whether they are likely to share them. Importantly, the current work shows that greater attention might exacerbate the viral spread of false claims because people often rely on their heuristic judgments of the news stories in which they first encountered a claim to determine sharing intentions. This result underscores the importance of revisiting regulatory and organizational strategies to combat misinformation. The current dissertation outlines how biometrics can be used as a robust method to identify news stories that are likely to give rise to viral claims (fake or otherwise), thereby enabling organizations to direct their fact-checking resources better. This dissertation also shows, across five studies, how brands and NPOs that are actively contributing to improving societal outcomes can better advertise their efforts. I study the role of attention in CSR (Corporate Social Responsibility) advertising. While normative reasoning suggests that providing consumers with more information about organizational efforts is better for improving consumer attitudes and behavior, we show that this is not always the case. Specifically, the current work explicates that while it is beneficial for brands to communicate their concrete resource contributions to a social cause in their CSR advertising, it is not always beneficial for NPOs to do so. The difference arises because when brands reveal a signal of resource commitment to the cause in a CSR ad, people notice this signal, and it makes people believe that the brand is more honest and sincere. On the other hand, when NPOs- often the ones working closest to the social causes on the ground - reveal their resource contributions to a cause in a CSR ad, people pay less attention to these signals in the ad. Consequently, they are less likely to infer any additional sincerity on the part of the NPO. / Business Administration/Marketing

Identification of an individual from discriminating features of the friction ridge surface is one of the oldest and most commonly used biometric techniques. Methods for identification span from tedious, although highly accurate, manual examination to much faster Automated Fingerprint Identification Systems (AFIS). While automatic fingerprint recognition has grown in popularity due to the speed and accuracy of matching minutia features of good quality plain-to-rolled prints, the performance is less than impressive when matching partial fingerprints. For some applications, including forensic analysis where partial prints come in the form of latent prints, it is not always possible to obtain high-quality image samples. Latent prints, which are lifted from a surface, are typically of low quality and low fingerprint surface area. As a result, the overlapping region in which to find corresponding features in the genuine matching ten-print is reduced; this in turn reduces the identification performance. Image quality also can vary substantially during image capture in applications with a high throughput of subjects having limited training, such as in border control. The rushed image capture leads to an overall acceptable sample being obtained where local image region quality may be low. We propose an improvement to the reliability of features detected in exemplar prints in order to reduce the likelihood of an unreliable overlapping region corresponding with a genuine partial print. A novel approach is proposed for detecting minutiae in low quality image regions. The approach has demonstrated an increase in match performance for a set of fingerprints from a well-known database. While the method is effective at improving match performance for all of the fingerprint images in the database, a more significant improvement is observed for a subset of low quality images. In addition, a novel method for fingerprint analysis using a sequence of fingerprint images is proposed. The approach uses the sequence of images to extract and track minutiae for temporal analysis during a single impression, reducing the variation in image quality during image capture. Instead of choosing a single acceptable image from the sequence based on a global measure, we examine the change in quality on a local level and stitch blocks from multiple images based on the optimal local quality measures. / Ph. D.

The streaming platform Twitch.tv, is a popular video streaming website where users can watch, communicate, and chat with streamers and other viewers live. Twitch and other live streaming websites typically offer viewers a chat window where the viewer can chat (in real-time) with the streamer and other viewers who are currently watching the stream. Research in live streaming platforms like Twitch and the effects of their various features are novel. The purpose of this study is to analyze how the sentiment of a live chat (positive or negative) and the stated or not stated disclosure of an advertisement posted in the chat may impact the viewer's perceptions of the posted advertisement. In order to answer this, this study used a series of scales to measure how visible the advertisements were in the chat, if participants were able to recognize that the advertisement in the live chat was an ad, participants' perceptions of the advertisements, usage statistics of their live video streaming habits, liking of certain product categories, and purchase intentions of the products shown in the chat. Eye-tracking biometric technology was used to track participants' visual attention to the advertisements shown in the live chat. A sample of 120 participants between the ages of 18-30 were randomly assigned to one of four conditions where the disclosure of advertisements (no disclosure and disclosure) and valence of the chat (positive or negative) were manipulated. Results revealed that participants assigned to the negative valence condition viewed the advertisements in the chat more than those who were assigned the positive valence condition. Additionally, no significant relationship was found to be associated with advertisement disclosure and advertisement visibility, how advertisement disclosure impacts advertisement perceptions, how chat valence impacts advertisement recognition, how chat valence impacts advertisement perceptions, or how product liking and chat valence influence purchase intentions. Implications for the use of the emotional contagion theory in live-streaming media and recommendations for advertising practitioners are discussed.

Securing information during its storage and transmission is an important and widely addressed issue. Generally, cryptographic techniques are used for information security. Cryptography requires long keys which need to be kept secret in order to protect the information. The drawback of cryptography is that these keys are not strongly linked to the user identity. In order to strengthen the link between the user's identity and his cryptographic keys, biometrics is combined with cryptography. In this thesis, we present various methods to combine biometrics with cryptography. With this combination, we also address the privacy issue of biometric systems: revocability, template diversity, and privacy protection are added to the biometric verification systems. Finally, we also present a protocol for generating and sharing biometrics based crypto-biometric session keys. These systems are evaluated on publicly available iris and face databases

The dissertation investigates the promises and challenges for application of cardiac signals in biometrics and affective computing, and noninvasive measurement of cardiac signals. We have mainly discussed two major cardiac signals: electrocardiogram (ECG), and photoplethysmogram (PPG). ECG and PPG signals hold strong potential for biometric authentications and identifications. We have shown that by mapping each cardiac beat from time domain to an angular domain using a limit cycle, intra-class variability can be significantly minimized. This is in contrary to conventional time domain analysis. Our experiments with both ECG and PPG signal shows that the proposed method eliminates the effect of instantaneous heart rate on the shape morphology and improves authentication accuracy. For noninvasive measurement of PPG beats, we have developed a systematic algorithm to extract pulse rate from face video in diverse situations using video magnification. We have extracted signals from skin patches and then used frequency domain correlation to filter out non-cardiac signals. We have developed a novel entropy based method to automatically select skin patches from face. We report beat-to-beat accuracy of remote PPG (rPPG) in comparison to conventional average heart rate. The beat-to-beat accuracy is required for applications related to heart rate variability (HRV) and affective computing. The algorithm has been tested on two datasets, one with static illumination condition and the other with unrestricted ambient illumination condition. Automatic skin detection is an intermediate step for rPPG. Existing methods always depend on color information to detect human skin. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. We have used LBP lacunarity based micro-textures features and a region growing algorithm to find skin pixels in an image. Our experiment shows that the proposed method is applicable universally to any image including near infra-red images. This finding helps to extend the domain of many application including rPPG. To the best of our knowledge, this is first such method that is independent of color cues. / Ph. D. / The heart is an integral part of the human body. With every beat, the heart continuously pumps oxygen-enriched blood to providing fuel to our cells and thus enabling life. The heartbeat is initiated by electrical signals generated in the heart muscles. This electrical activity, which are often governed by our autonomic nervous system, can be measured directly by electrocardiogram (ECG) using advanced and often obtrusive instrumentation. Photoplethysmogram (PPG), on the other hand, measures how the blood volume changes and can be readily measured with inexpensive instrumentation at certain locations (e.g. at the fingertip). The ECG and PPG are widely used cardiac signals in medical science for diagnosis and health monitoring. But, these signals hold greater potential than just its medical diagnostic applications. In this work, we have mainly investigated if these signals can be used to identify an individual. Every human heart differs by their size, shape, locations inside body, and internal structure. This motivated us to represent the signals using a mathematical model and use machine learning algorithm to identify individual persons. We have discussed how our method improves the identification accuracy and can be used with current biometric methods like fingerprint in our phone. The measurement procedures of cardiac signals are often cumbersome and need instruments which may not be available outside medical facilities. Therefore, we have investigated alternative method of remote photoplethysmography (rPPG) that are relatively inexpensive and unobtrusive. In this dissertation, we have used face video of an individual to extract the heart rate information. The flow of blood causes small changes in the color of face skin. This is not visible to human eyes without digital magnification, but we have shown how knowledge of distinct behavior of human heart rate and use of advanced computer vision algorithms helped us to extract vital signals like heart rate with a significant accuracy. In addition, to measure rPPG using face video, we integrated a method for automatic detection of skin from images and videos. Existing skin detection methods depended on color information which is not always available within available video sources. We have developed a novel standalone skin detection method to show that it is not necessary to have color cues for skin detection. Our method relies on the context and the texture based appearance of skin. To the best of our knowledge, this is first such method that is independent of color cues. In summary, the dissertation investigates the promises and challenges for application of cardiac signals in biometrics and nonobtrusive measurement of cardiac signals using face video.

La sécurité est un enjeu majeur de notre société numérique. En règle générale, les techniques cryptographiques sont utilisées pour sécuriser l'information avec des clés cryptographiques. Un inconvénient majeur de ces systèmes est le faible lien entre les clés et l’utilisateur. Avec la biométrie on a une preuve plus forte de la présence physique d’un individu, mais ces systèmes possèdent aussi leurs inconvénients, tels que la non-révocabilité ainsi que le potentiel de compromettre notre vie privée. Un axe de recherche multidisciplinaire se profile depuis 1998, la crypto-biométrie. Dans cette thèse des solutions innovantes sont proposées pour améliorer la sécurité tout en protégeant notre vie privée. Plusieurs systèmes crypto-biométriques sont proposés, tels que la biométrie révocable, des systèmes de régénérations de clés crypto-biométriques, ainsi qu’une proposition pratique d’un protocole d'authentification. Ces systèmes sont évaluées sur des bases de données publiques d'images de visage et d'iris / Securing information during its storage and transmission is an important and widely addressed issue. Generally, cryptographic techniques are used for information security. Cryptography requires long keys which need to be kept secret in order to protect the information. The drawback of cryptography is that these keys are not strongly linked to the user identity. In order to strengthen the link between the user's identity and his cryptographic keys, biometrics is combined with cryptography. In this thesis, we present various methods to combine biometrics with cryptography. With this combination, we also address the privacy issue of biometric systems: revocability, template diversity, and privacy protection are added to the biometric verification systems. Finally, we also present a protocol for generating and sharing biometrics based crypto-biometric session keys. These systems are evaluated on publicly available iris and face databases

Made available in DSpace on 2015-03-03T15:48:40Z (GMT). No. of bitstreams: 1 FernandoP_TESE.pdf: 2701691 bytes, checksum: 2a3af30ede2c717ab23b1c7dc03a128a (MD5) Previous issue date: 2013-05-24 / This work discusses the application of techniques of ensembles in multimodal recognition systems development in revocable biometrics. Biometric systems are the future identification techniques and user access control and a proof of this is the constant increases of such systems in current society. However, there is still much advancement to be developed, mainly with regard to the accuracy, security and processing time of such systems. In the search for developing more efficient techniques, the multimodal systems and the use of revocable biometrics are promising, and can model many of the problems involved in traditional biometric recognition. A multimodal system is characterized by combining different techniques of biometric security and overcome many limitations, how: failures in the extraction or processing the dataset. Among the various possibilities to develop a multimodal system, the use of ensembles is a subject quite promising, motivated by performance and flexibility that they are demonstrating over the years, in its many applications. Givin emphasis in relation to safety, one of the biggest problems found is that the biometrics is permanently related with the user and the fact of cannot be changed if compromised. However, this problem has been solved by techniques known as revocable biometrics, which consists of applying a transformation on the biometric data in order to protect the unique characteristics, making its cancellation and replacement. In order to contribute to this important subject, this work compares the performance of individual classifiers methods, as well as the set of classifiers, in the context of the original data and the biometric space transformed by different functions. Another factor to be highlighted is the use of Genetic Algorithms (GA) in different parts of the systems, seeking to further maximize their eficiency. One of the motivations of this development is to evaluate the gain that maximized ensembles systems by different GA can bring to the data in the transformed space. Another relevant factor is to generate revocable systems even more eficient by combining two or more functions of transformations, demonstrating that is possible to extract information of a similar standard through applying different transformation functions. With all this, it is clear the importance of revocable biometrics, ensembles and GA in the development of more eficient biometric systems, something that is increasingly important in the present day / O presente trabalho aborda a aplica??o de t?cnicas de comit?s de classificadores no desenvolvimento de sistemas de reconhecimento multimodais em biometrias revog?veis. Sistemas biom?tricos s?o o futuro das t?cnicas de identifica??o e controle de acesso de usu?rios, prova disso, s?o os aumentos constantes de tais sistemas na sociedade atual. Por?m, ainda existem muitos avan?os a serem desenvolvidos, principalmente no que se refere ? acur?cia, seguran?a e tempo de processamento de tais sistemas. Na busca por desenvolver t?cnicas mais eficientes, os sistemas multimodais e a utiliza??o de biometrias revog?veis mostram-se promissores, podendo contornar muitos dos problemas envolvidos no reconhecimento biom?trico tradicional. Um sistema multimodal ? caracterizado por combinar diferentes t?cnicas de seguran?a biom?trica e com isso, superar muitas limita- ??es, como: falhas de extra??o ou processamento dos dados. Dentre as v?rias possibilidades de se desenvolver um sistema multimodal, a utiliza??o de comit?s de classificadores ? um assunto bastante promissor, motivado pelo desempenho e flexibilidade que os mesmos v?m demonstrando ao longo dos anos, em suas in?meras aplica??es. Dando ?nfase em rela- ??o ? seguran?a, um dos maiores problemas encontrados se deve as biometrias estarem relacionadas permanentemente com o usu?rio e o fato de n?o poderem ser alteradas caso comprometidas. No entanto, esse problema vem sendo solucionado por t?cnicas conhecidas como biometrias revog?veis, as quais consistem em aplicar uma transforma??o sobre os dados biom?tricos de forma a proteger as caracter?sticas originais, possibilitando seu cancelamento e substitui??o. Com o objetivo de contribuir com esse importante tema, esse trabalho compara o desempenho de m?todos de classifica??es individuais, bem como conjunto de classificadores, no contexto dos dados originais e no espa?o biom?trico transformado por diferentes fun??es. Outro fator a se destacar, ? o uso de Algoritmos Gen?ticos (AGs) em diferentes partes dos sistemas, buscando maximizar ainda mais a efici?ncia dos mesmos. Uma das motiva??es desse desenvolvimento ? avaliar o ganho que os sistemas de comit?s maximizados por diferentes AGs podem trazer aos dados no espa?o transformado. Tamb?m busca-se gerar sistemas revog?veis ainda mais eficientes, atrav?s da combina??o de duas ou mais fun??es de transforma??o revog?veis, demonstrando que ? poss?vel extrair informa??es complementares de um mesmo padr?o atrav?s de tais procedimentos. Com tudo isso, fica claro a import?ncia das biometrias revog?veis, comit?s de classificadores e AGs, no desenvolvimento de sistemas biom?tricos mais eficientes, algo que se mostra cada vez mais importante nos dias atuais

M. Tech. (Department of Information and Communication Technology, Faculty of Applied and Computer Sciences), Vaal University of Technology. / Mobile devices are rapidly becoming a key computing platform, transforming how people access business and personal information. Accessing business and personal data using mobile devices requires authentication that is secure. The world is rapidly becoming connected and all users of mobile devices need to be clear regarding individual data security. As a result, biometrics for mobile devices has come into existence. Biometric technology can be applied on mobile devices to improve the trustworthiness of wireless services. Furthermore, it is of great importance and necessary to start paying attention to and investing in mobile biometric technologies, as they are quickly turning into tools of choice for productivity. In the literature review, it shows that few studies measured the acceptance of biometric authentication technology on mobile devices. This study seeks to find out the perceptions as to the acceptance of biometric authentication technology on mobile devices. TAM2 was used as the foundation for generating the hypothesis and developing the conceptual framework for this study. This quantitative study used a survey-based questionnaire to collect data from 305 participants. The simple random sampling technique was used to select participants for this study. The response rate was 98% of the expected population, which was a total of 302 valid responses. A descriptive analysis was deployed to provide a description of respondents’ demographic characteristics. SPSS was used to compute the multiple regressions in order to evaluate the research hypotheses. The findings of this study revealed that perceived humanness, perceived interactivity, perceived social presence, perceived ease of use and subjective social norm, and perceived usefulness and trust are important determinants of customers’ intention to accept and use mobile biometric devices. It was found that reliability is a good predictor of trust. On the other hand privacy, identity theft and combining data are also important determinants of trust. This work can be used to strengthen biometric authentication technology in-cooperation with mobile devices for simplicity of use. Since most mobile devices are used for personal and business information, further research on the acceptance of biometric authentication technology on mobile devices is needed.

The use of mobile devices has extended to all areas of human life and has changed the way people work and socialize. Mobile devices are susceptible to getting lost, stolen, or compromised. Several approaches have been adopted to protect the information stored on these devices. One of these approaches is user authentication. The two most popular methods of user authentication are knowledge based and token based methods but they present different kinds of problems. Biometric authentication methods have emerged in recent years as a way to deal with these problems. They use an individual’s unique characteristics for identification and have proven to be somewhat effective in authenticating users. Biometric authentication methods also present several problems. For example, they aren’t 100% effective in identifying users, some of them are not well perceived by users, others require too much computational effort, and others require special equipment or special postures by the user. Ultimately their implementation can result in unauthorized use of the devices or the user being annoyed by the implementation. New ways of interacting with mobile devices have emerged in recent years. This makes it necessary for authentication methods to adapt to these changes and take advantage of them. For example, the use of touchscreens has become prevalent in mobile devices, which means that biometric authentication methods need to adapt to it. One important aspect to consider when adopting these new methods is their acceptance of these methods by users. The Technology Acceptance Model (TAM) states that system use is a response that can be predicted by user motivation. This work presents an authentication method that can constantly verify the user’s identity which can help prevent unauthorized use of a device or access to sensitive information. The goal was to authenticate people while they used their fingers to interact with their touchscreen mobile devices doing ordinary tasks like vertical and horizontal scrolling. The approach used six biometric traits to do the authentication. The combination of those traits allowed for authentication at the beginning and at the end of a finger stroke. Support Vector Machines were employed and the best results obtained show Equal Error Rate values around 35%. Those results demonstrate the potential of the approach to verify a person’s identity. Additionally, this works tested the acceptance of the approach among participants, which can influence its eventual adoption. An acceptance level of 80% was obtained which compares favorably against other behavioral biometric approaches.

Encephalogram (EEG) devices are one of the active research areas in human-computer interaction (HCI). They provide a unique brain-machine interface (BMI) for interacting with a growing number of applications. EEG devices interface with computational systems, including traditional desktop computers and more recently mobile devices. These computational systems can be targeted by malicious users. There is clearly an opportunity to leverage EEG capabilities for increasing the efficiency of access control mechanisms, which are the first line of defense in any computational system. Access control mechanisms rely on a number of authenticators, including “what you know”, “what you have”, and “what you are”. The “what you are” authenticator, formally known as a biometrics authenticator, is increasingly gaining acceptance. It uses an individual’s unique features such as fingerprints and facial images to properly authenticate users. An emerging approach in physiological biometrics is cognitive biometrics, which measures brain’s response to stimuli. These stimuli can be measured by a number of devices, including EEG systems. This work shows an approach to authenticate users interacting with their computational devices through the use of EEG devices. The results demonstrate the feasibility of using a unique hard-to-forge trait as an absolute biometrics authenticator by exploiting the signals generated by different areas of the brain when exposed to visual stimuli. The outcome of this research highlights the importance of the prefrontal cortex and temporal lobes to capture unique responses to images that trigger emotional responses. Additionally, the utilization of logarithmic band power processing combined with LDA as the machine learning algorithm provides higher accuracy when compared against common spatial patterns or windowed means processing in combination with GMM and SVM machine learning algorithms. These results continue to validate the value of logarithmic band power processing and LDA when applied to oscillatory processes.