Active detection of age groups based on touch interaction

This paper studies user classification into children and adults according to their interaction with touchscreen devices. We analyse the performance of two sets of features derived from the Sigma-Lognormal theory of rapid human movements and global characterization of touchscreen interaction. We propose an active detection approach aimed to continuously monitorize the user patterns. The experimentation is conducted on a publicly available database with samples obtained from 89 children between 3 and 6 years old and 30 adults. We have used Support Vector Machines algorithm to classify the resulting features into age groups. The sets of features are fused at score level using data from smartphones and tablets. The results, with correct classification rates over 96%, show the discriminative ability of the proposed neuromotorinspired features to classify age groups according to the interaction with touch devices. In active detection setup, our method is able to identify a child using only 4 gestures in averageThis work was funded by the project CogniMetrics (TEC2015-70627-R) and Bio-Guard (Ayudas Fundación BBVA a Equipos de Investigación Científica 2017

Autenticación continua de usuario basada en interacción táctil

Hoy en día, con el auge continuo de la tecnología, cualquier aspecto relacionado con la seguridad adquiere un grado trascendental de importancia. Disponemos de información vital muy sensible en los nuevos dispositivos tecnológicos, ya sean ordenadores, tablets o smartphones. Dicha información debe ser protegida frente a cualquier usuario que no sea legítimo. Para ello, en los últimos años se han utilizado claves, tokens y otros métodos. La parte negativa es que muchos ofrecen un alto porcentaje de vulnerabilidad, además de ser soluciones difícilmente escalables a una vida diaria en la que debemos gestionar un elevado número de servicios y plataformas que requieren protección. Por lo tanto, el reconocimiento biométrico alcanza significativa importancia en este sector, ya que no solo obtiene grandes resultados de cara a proteger la información, sino que, haciendo uso de una parte única correspondiente a nosotros, elimina la necesidad de memorizar una combinación previa o portar un token determinado. Dentro del reconocimiento biométrico, existen diferentes métodos relacionados con cómo se evalúa y/o monitoriza la identidad del usuario. De especial interés para este trabajo es el denominado autenticación continua. Este procedimiento consiste en aplicar una serie de autenticaciones de usuario periódicas de cara a ofrecer mayor robustez, monitorizando de forma constante si el usuario que hace uso del dispositivo analizado es el correcto. En este trabajo realizado se reflejan detalladamente una serie de estudios y análisis sobre la autenticación de usuarios, focalizándose únicamente en dispositivos con pantalla táctil, en este caso smartphones. Para llevar a cabo este objetivo, se han utilizado medidas obtenidas previamente por diversas fuentes en diferentes bases de datos. Además, se ha hecho uso de algoritmos de clasificación de patrones basados en Máquinas de Vector Soporte y Modelos de Mezclas Gaussianas. Dichos algoritmos explotan la información discriminativa y estadística, para posteriormente combinar sus características mediante la fusión, mejorando de manera notoria los resultados obtenidos. Finalmente, se ha aplicado el algoritmo denominado Quickest Change Detection, el cual incrementa la eficacia del desarrollo en términos de latencia y probabilidad de falsa detección de usuarios. Esto se ha logrado teniendo en cuenta los resultados obtenidos anteriormente al instante en el que el usuario registra nuevos datos en la aplicación.Nowadays, with the continuous rise of technology, any aspect related to security acquires a transcendental degree of importance. We have vital information in the new technological devices, whether computers, tablets or smartphones. This information must be protected against any user that is not legitimate. For this, keys, tokens and other methods have been used in recent years. The negative part is that many offer a high percentage of vulnerability, in addition to being hard to scale solutions to a daily life in which we must manage a large number of services and platforms that require protection. Therefore, biometric recognition reaches significant importance in this sector, since it not only obtains great results in order to protect the information, but, making use of a unique part corresponding to us, eliminates the need to memorize a previous combination or carry a certain token. Within the biometric recognition, there are different methods related to how the identity of the user is evaluated and/or monitored. Of special interest for this work is the so-called continuous authentication. This procedure consists of applying a series of periodic user authentications in order to offer greater robustness, constantly monitoring if the user that makes use of the analyzed device is the correct one. In this work, a series of studies and analyzes on user authentication are reflected in detail, focusing only on touchscreen devices, in this case smartphones. To carry out this objective, previously obtained measurements have been used by different sources in different databases. In addition, pattern classification algorithms based on Vector Support Machines and Gaussian Mixture Models have been used. These algorithms exploit the discriminative and statistical information, to later combine their characteristics by means of fusion, improving in a noticeable way the obtained results. Finally, the algorithm called Quickest Change Detection has been applied, which increases the effectiveness of the development in terms of latency and the probability of false detection of users. This has been achieved by taking into account the results previously obtained at the moment in which the user registers new data in the application

Deep Learning Based Novelty Detection

Given a set of image instances from known classes, the goal of novelty detection is to determine whether an observed image during inference belongs to one of the known classes. In this thesis, deep learning-based approaches to solve novelty detection are studied under four different settings. In the first two settings, availability of out-of- distributional data (OOD) is assumed. With this assumption, novelty detection can be studied for cases where there are multiple known classes and a single known class separately. The thesis further explores this problem in a more constrained setting where only the data from known classes are considered for training. Finally, we study a practical application of novelty detection in mobile Active Authentication (AA) where latency and efficiency are as important as the detection accuracy