Detección del punto de observación del usuario en la pantalla mediante una webcam

En este Trabajo Fin de Grado se estudian diferentes rastreadores oculares que están en el mercado, puntualizando cuáles de ellos pueden ser de utilidad. De igual manera, este trabajo trata de conseguir implementar un rastreador ocular que identifique el punto de observación de un usuario, mediante una webcam, en la pantalla del ordenador. Para ello, se comienza investigando los diferentes tipos de rastreadores oculares, estudiando cuales son de mayor utilidad para este trabajo. Una vez descubiertos aquellos que pueden ser de ayuda, se contemplan distintos software con licencia libre de uso y que utilicen una cámara web para captar las imágenes. Tras este estudio se ha centrado la atención en tres software distintos, realizando un análisis a fondo de su funcionamiento. Tras no conseguir resultados suficientes, se ha implementado un nuevo programa que, a partir de un conjunto de puntos característicos y asumiendo una serie de premisas, detecta con cierta fiabilidad el punto de observación del usuario en la pantalla del ordenador. Para conseguir el punto de observación, es necesaria una extracción de puntos característicos y una calibración previa donde se detecta la distancia que se encuentra entre el usuario y la pantalla. Una vez realizado esto, se estudia el problema separando la posición horizontal y vertical de la pupila del usuario. Para estudiar la posición horizontal se calculan las proyecciones de las pupilas, mientras que la posición vertical se realiza por medio de un serie de cómputos trigonométricos. El programa trata cada uno de los dos ojos por separado. Tras este estudio se realiza una serie de pruebas para comprobar la fiabilidad del programa, dejando como trabajo futuro aquellos puntos que se han encontrado deficientes en la estimación

Generación Sintética de Secuencias Temporales a través de Redes Neuronales Profundas

Máster Universitario en Ingeniería de TelecomunicaciónGracias a los algoritmos de Aprendinzaje Automático, como son las redes neuronales profundas, se ha producido un gran avance en el estudio de secuencias temporales. Dentro de la biometría, uno de los campos más importantes para verificar la identidad de un usuario es su firma y escritura manuscrita. Estos rasgos son difíciles de estudiar debido a la escasez de sus datos y variabilidad. Un individuo nunca realiza su firma dos veces de igual manera, aun así existen diferentes características que permiten distinguir a cada individuo. Uno de los grandes inconvenientes que muestra este ámbito, especialmente la firma manuscrita, es la escasez de datos que existe debido a los problemas legales y al elevado coste del proceso de captura de los mismos. En este trabajo se propone el estudio y desarrollo de sistemas para la generación sintética de firma y escritura dinámica, con el objetivo de solventar la escasez de datos en este campo. El método propuesto está basado en la última tecnología disponible en el ámbito de las redes neuronales profundas y consta de dos módulos principales: el módulo de segmentación a nivel de trazos y el módulo de síntesis. En primer lugar, el módulo de segmentación a nivel de trazos particiona las secuencias temporales de la firma y escritura en tramos de menor número de muestras, adecuando así los datos para el siguiente módulo. En segundo lugar, el módulo de síntesis consta de un Variational Autoencoder (VAE) que realiza la tarea de síntesis de firma y escritura manuscrita on-line. Se trata de un sistema entrenado a nivel de trazos. Una de las mayores ventajas que presenta el modelo es su impersonalidad, es decir, el modelo entrenado es adaptable para cualquier usuario y cualquier situación de firma y escritura manuscrita on-line. A su vez, al ser entrenado a nivel de trazos permite la síntesis de nuevas muestras con un gran nivel de detalle y variabilidad. El modelo de síntesis ha sido probado en dos escenarios de escritura manuscrita: contraseñas y firmas on-line obteniendo muy buenos resultados tanto a nivel visual como de funciones temporales. Por último, se ha demostrado la gran utilidad del método propuesto en escenarios con escasez de datos, mejorando las tasas de rendimiento de los sistemas automáticos de firma manuscrita on-lin

Mobile Device Background Sensors: Authentication vs Privacy

The increasing number of mobile devices in recent years has caused the collection of a large amount of personal information that needs to be protected. To this aim, behavioural biometrics has become very popular. But, what is the discriminative power of mobile behavioural biometrics in real scenarios? With the success of Deep Learning (DL), architectures based on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM), have shown improvements compared to traditional machine learning methods. However, these DL architectures still have limitations that need to be addressed. In response, new DL architectures like Transformers have emerged. The question is, can these new Transformers outperform previous biometric approaches? To answers to these questions, this thesis focuses on behavioural biometric authentication with data acquired from mobile background sensors (i.e., accelerometers and gyroscopes). In addition, to the best of our knowledge, this is the first thesis that explores and proposes novel behavioural biometric systems based on Transformers, achieving state-of-the-art results in gait, swipe, and keystroke biometrics. The adoption of biometrics requires a balance between security and privacy. Biometric modalities provide a unique and inherently personal approach for authentication. Nevertheless, biometrics also give rise to concerns regarding the invasion of personal privacy. According to the General Data Protection Regulation (GDPR) introduced by the European Union, personal data such as biometric data are sensitive and must be used and protected properly. This thesis analyses the impact of sensitive data in the performance of biometric systems and proposes a novel unsupervised privacy-preserving approach. The research conducted in this thesis makes significant contributions, including: i) a comprehensive review of the privacy vulnerabilities of mobile device sensors, covering metrics for quantifying privacy in relation to sensitive data, along with protection methods for safeguarding sensitive information; ii) an analysis of authentication systems for behavioural biometrics on mobile devices (i.e., gait, swipe, and keystroke), being the first thesis that explores the potential of Transformers for behavioural biometrics, introducing novel architectures that outperform the state of the art; and iii) a novel privacy-preserving approach for mobile biometric gait verification using unsupervised learning techniques, ensuring the protection of sensitive data during the verification process

Cuticle Structure in Relation to Chemical Composition: Re-assessing the Prevailing Model

The surface of most aerial plant organs is covered with a cuticle that provides protection against multiple stress factors including dehydration. Interest on the nature of this external layer dates back to the beginning of the 19th century and since then, several studies facilitated a better understanding of cuticular chemical composition and structure. The prevailing undertanding of the cuticle as a lipidic, hydrophobic layer which is independent from the epidermal cell wall underneath stems from the concept developed by Brongniart and von Mohl during the first half of the 19th century. Such early investigations on plant cuticles attempted to link chemical composition and structure with the existing technologies, and have not been directly challenged for decades. Beginning with a historical overview about the development of cuticular studies, this review is aimed at critically assessing the information available on cuticle chemical composition and structure, considering studies performed with cuticles and isolated cuticular chemical components. The concept of the cuticle as a lipid layer independent from the cell wall is subsequently challenged, based on the existing literature, and on new findings pointing toward the cell wall nature of this layer, also providing examples of different leaf cuticle structures. Finally, the need for a re-assessment of the chemical and structural nature of the plant cuticle is highlighted, considering its cell wall nature and variability among organs, species, developmental stages, and biotic and abiotic factors during plant growth

Mobile Keystroke Biometrics Using Transformers

Among user authentication methods, behavioural biometrics has proven to be effective against identity theft as well as user-friendly and unobtrusive. One of the most popular traits in the literature is keystroke dynamics due to the large deployment of computers and mobile devices in our society. This paper focuses on improving keystroke biometric systems on the free-text scenario. This scenario is characterised as very challenging due to the uncontrolled text conditions, the influence of the user's emotional and physical state, and the in-use application. To overcome these drawbacks, methods based on deep learning such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been proposed in the literature, outperforming traditional machine learning methods. However, these architectures still have aspects that need to be reviewed and improved. To the best of our knowledge, this is the first study that proposes keystroke biometric systems based on Transformers. The proposed Transformer architecture has achieved Equal Error Rate (EER) values of 3.84\% in the popular Aalto mobile keystroke database using only 5 enrolment sessions, outperforming by a large margin other state-of-the-art approaches in the literature.Comment: 6 pages, 6 figure

TypeFormer: Transformers for Mobile Keystroke Biometrics

The broad usage of mobile devices nowadays, the sensitiveness of the information contained in them, and the shortcomings of current mobile user authentication methods are calling for novel, secure, and unobtrusive solutions to verify the users' identity. In this article, we propose TypeFormer, a novel Transformer architecture to model free-text keystroke dynamics performed on mobile devices for the purpose of user authentication. The proposed model consists in Temporal and Channel Modules enclosing two Long Short-Term Memory (LSTM) recurrent layers, Gaussian Range Encoding (GRE), a multi-head Self-Attention mechanism, and a Block-Recurrent structure. Experimenting on one of the largest public databases to date, the Aalto mobile keystroke database, TypeFormer outperforms current state-of-the-art systems achieving Equal Error Rate (EER) values of 3.25% using only 5 enrolment sessions of 50 keystrokes each. In such way, we contribute to reducing the traditional performance gap of the challenging mobile free-text scenario with respect to its desktop and fixed-text counterparts. Additionally, we analyse the behaviour of the model with different experimental configurations such as the length of the keystroke sequences and the amount of enrolment sessions, showing margin for improvement with more enrolment data. Finally, a cross-database evaluation is carried out, demonstrating the robustness of the features extracted by TypeFormer in comparison with existing approaches

M-GaitFormer: Mobile biometric gait verification using Transformers

Mobile devices such as smartphones and smartwatches are part of our everyday life, acquiring large amount of personal information that needs to be properly secured. Among the different authentication techniques, behavioural biometrics has become a very popular method as it allows authentication in a non-intrusive and continuous way. This study proposes M-GaitFormer, a novel mobile biometric gait verification system based on Transformer architectures. This biometric system only considers the accelerometer and gyroscope data acquired by the mobile device. A complete analysis of the proposed M-GaitFormer is carried out using the popular available databases whuGAIT and OU-ISIR. M-GaitFormer achieves Equal Error Rate (EER) values of 3.42% and 2.90% on whuGAIT and OU-ISIR, respectively, outperforming other state-of-the-art approaches based on popular Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

GaitPrivacyON: Privacy-Preserving Mobile Gait Biometrics using Unsupervised Learning

Numerous studies in the literature have already shown the potential of biometrics on mobile devices for authentication purposes. However, it has been shown that, the learning processes associated to biometric systems might expose sensitive personal information about the subjects. This study proposes GaitPrivacyON, a novel mobile gait biometrics verification approach that provides accurate authentication results while preserving the sensitive information of the subject. It comprises two modules: i) two convolutional Autoencoders with shared weights that transform attributes of the biometric raw data, such as the gender or the activity being performed, into a new privacy-preserving representation; and ii) a mobile gait verification system based on the combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with a Siamese architecture. The main advantage of GaitPrivacyON is that the first module (convolutional Autoencoders) is trained in an unsupervised way, without specifying the sensitive attributes of the subject to protect. Two experimental studies have been examinated: i) MotionSense and MobiAct databases; and ii) OU-ISIR database. The experimental results achieved suggest the potential of GaitPrivacyON to significantly improve the privacy of the subject while keeping user authentication results higher than 96.6% Area Under the Curve (AUC). To the best of our knowledge, this is the first mobile gait verification approach that considers privacy-preserving methods trained in an unsupervised way

SwipeFormer: Transformers for mobile touchscreen biometrics

The growing number of mobile devices over the past few years brings a large amount of personal information, which needs to be properly protected. As a result, several mobile authentication methods have been developed. In particular, behavioural biometrics has become one of the most relevant methods due to its ability to extract the uniqueness of each subject in a secure, non-intrusive, and continuous way. This article presents SwipeFormer, a novel Transformer-based system for mobile subject authentication by means of swipe gestures in an unconstrained scenario (i.e., subjects could use their personal devices freely, without restrictions on the direction of swipe gestures or the position of the device). Our proposed system contains two modules: (i) a Transformer-based feature extractor, and (ii) a similarity computation module. Mobile data from the touchscreen and different background sensors (accelerometer and gyroscope) have been studied, including in the analysis both Android and iOS operating systems. A complete analysis of SwipeFormer is carried out using an in-house large-scale database acquired in unconstrained scenarios. In these operational conditions, SwipeFormer achieves Equal Error Rate (EER) values of 6.6% and 3.6% on Android and iOS respectively, outperforming the state of the art. In addition, we evaluate SwipeFormer on the popular publicly available databases Frank DB and HuMIdb, achieving EER values of 11.0% and 5.0% respectively, outperforming previous approaches under the same experimental setup

Utilização de resíduos provenientes da construção e demolição de reforma residencial como agregado miúdo para fabricação de concreto

A exploração infrene por recursos naturais para aplicação na construção civil é foco por estudos que visam maximizar a reutilização de resíduos que seriam descartados inadequadamente. Dessa maneira, esta pesquisa visa elaborar um concreto utilizando resíduos de construção e demolição como agregado miúdo, de forma a obter uma boa aplicabilidade na construção civil. A diversidade de resíduos coletados pode desencadear reações com os demais compostos do concreto, alterando a sua qualidade e consequentemente o resultado final. A aplicabilidade do concreto na construção civil dependerá, dentre outros fatores, da resistência aos esforços de compressão que são analisados em equipamento apropriado. Os resultados apresentaram a possibilidade de inserção do material em estudo em algumas modalidades de concreto, desde que atendam aos requisitos das normas vigentes