4 research outputs found

    Arquitectura de percepci贸n bioinspirada basada en atenci贸n para un robot social

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    La atenci贸n desempe帽a un papel fundamental, tanto para los seres humanos como para los sistemas artificiales, ya que es una habilidad crucial que nos permite interactuar de manera efectiva con nuestro entorno. Desde la infancia hasta la edad adulta, la atenci贸n nos ayuda a concentrarnos en est铆mulos relevantes, procesar informaci贸n de manera eficiente y responder a est铆mulos emocionales y sociales. Adem谩s, de influir en aspectos importantes de nuestras vidas, como el aprendizaje y las interacciones sociales. La implementaci贸n de mecanismos de atenci贸n en sistemas artificiales tiene como objetivo aprovechar los beneficios de esta habilidad fundamental. Esto se traduce en una mejora en el procesamiento de informaci贸n, la toma de decisiones y la interacci贸n con el entorno. La atenci贸n en sistemas artificiales es un 谩rea de investigaci贸n en constante desarrollo, con el prop贸sito de mejorar la capacidad de los sistemas inteligentes en diversas aplicaciones. Uno de los campos donde m谩s se ha estudiado el concepto de la atenci贸n es en visi贸n artificial, en la cual se utiliza para resaltar regiones relevantes en las im谩genes, lo que mejora el an谩lisis y el reconocimiento de objetos, mientras que en la rob贸tica, la atenci贸n permite a los robots enfocarse en objetos o eventos espec铆ficos, mejorando su capacidad de reacci贸n y ejecuci贸n de tareas. Por este motivo, en este trabajo se propone un sistema de percepci贸n bioinspirado basado en atenci贸n dise帽ado para mejorar la interacci贸n humano-robot. Este sistema est谩 dise帽ado para localizar el foco de atenci贸n del robot en cada momento teniendo en cuenta la tarea actual, los est铆mulos disponibles y el estado interno del robot. El sistema integra fen贸menos bioinspirados como la inhibici贸n al retorno, la relocalizaci贸n del foco de atenci贸n dependiendo de los est铆mulos, los conceptos de atenci贸n sostenida y puntual para el cambio en el foco de atenci贸n y de agregaci贸n de est铆mulos de forma ex贸gena y end贸gena de forma independiente. Adem谩s, se ha integrado en una plataforma rob贸tica y se ha validado su funcionamiento en diferentes aplicaciones. Este trabajo se ha abordado desde dos perspectivas: la ampliaci贸n de las capacidades perceptuales del robot y la mejora de la interacci贸n gracias a la integraci贸n de la atenci贸n en la arquitectura software de las plataformas rob贸ticas. Para ello, en este trabajo se han investigado los est铆mulos m谩s relevantes para la atenci贸n en humanos y su integraci贸n en el 谩mbito de la rob贸tica y como realizar la agregaci贸n y fusi贸n multisensorial de estos desde un punto de vista basado en la atenci贸n, consiguiendo una representaci贸n del entorno y seleccionando la posici贸n del foco de atenci贸n en cada momento. Por otro lado, se ha investigado la relevancia de la integraci贸n de este sistema artificial a una plataforma rob贸tica en lo que respecta a la interacci贸n humano-robot, lo que ha dado lugar a un estudio que explora esta idea.Attention plays a fundamental role for both humans and artificial systems, as it is a crucial skill that enables us to interact effectively with our environment. From childhood to adulthood, attention helps us to focus on relevant stimuli, process information efficiently, and respond to emotional and social stimuli. It also influences important aspects of our lives, such as learning and social interactions. The implementation of attention mechanisms in artificial systems aims to take advantage of the benefits of this fundamental ability. This translates into improved information processing, decision making and interaction with the environment. Attention in artificial systems is an area of research in constant development, with the purpose of improving the capacity of intelligent systems in various applications. The fields where the concept of attention has been most studied are computer vision and robotics. In computer vision, attention is used to highlight relevant areas in images, which improves object analysis and recognition, while in robotics, attention allows robots to focus on specific objects or events, improving their ability to react and perform tasks. For this reason, this work proposes a bio-inspired attention-based perception system designed to improve human-robot interaction. This system is designed to locate the focus of attention of the robot at each moment, taking into account the current task, the available stimuli and the internal state of the robot.Moreover, the architecture integrates bioinspired concepts such as return inhibition, stimulus-dependent relocation of the focus of attention, the concepts of sustained and punctual attention for the shift in the focus of attention and the aggregation of exogenous and endogenous stimuli independently are integrated. In addition to this, it has been integrated into a robotic platform, and its performance has been validated in different applications. This work has been approached from two perspectives: the increase of the perceptual capabilities of the robot and the improvement of the interaction thanks to the integration of attention in the software architecture of robotic platforms. To this end, in this work, we have investigated the most relevant stimuli for attention in humans and their integration in the robotics environment, and how to perform the aggregation and multisensory fusion of these from an attention-based point of view, achieving a representation of the environment and selecting the position of the focus of attention at each moment. On the other hand, we have investigated the relevance of the integration of this artificial system to a robotic platform in terms of human-robot interaction, leading to a study that explores this idea.Programa de Doctorado en Ingenier铆a El茅ctrica, Electr贸nica y Autom谩tica por la Universidad Carlos III de MadridPresidente: Antonio Fern谩ndez Caballero.- Secretario: Concepci贸n Alicia Monje Micharet.- Vocal: Plinio Moreno L贸pe

    Advanced Biometrics with Deep Learning

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    Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others

    Non-cooperative iris recognition

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    The dramatic growth in practical applications for iris biometrics has been accompanied by relevant developments in the underlying algorithms and techniques. Along with the research focused on near-infrared images captured with subject cooperation, e orts are being made to minimize the trade-o between the quality of the captured data and the recognition accuracy on less constrained environments, where images are obtained at the visible wavelength, at increased distances, over simpli ed acquisition protocols and adverse lightning conditions. At a rst stage, interpolation e ects on normalization process are addressed, pointing the outcomes in the overall recognition error rates. Secondly, a couple of post-processing steps to the Daugman's approach are performed, attempting to increase its performance in the particular unconstrained environments this thesis assumes. Analysis on both frequency and spatial domains and nally pattern recognition methods are applied in such e orts. This thesis embodies the study on how subject recognition can be achieved, without his cooperation, making use of iris data captured at-a-distance, on-the-move and at visible wavelength conditions. Widely used methods designed for constrained scenarios are analyzed.Funda莽茫o para a Ci锚ncia e a Tecnologia (FCT

    Improving the Performance of Iris Recogniton System Using Eyelids and Eyelashes Detection and Iris Image Enhancement

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