Desarrollo de un sistema de reconocimiento facial

El objetivo principal alrededor del cual se desenvuelve este proyecto es el desarrollo de un sistema de reconocimiento facial. Entre sus objetivos específicos se encuentran: realizar una primera aproximación sobre las técnicas de reconocimiento facial existentes en la actualidad, elegir una aplicación donde pueda ser útil el reconocimiento facial, diseñar y desarrollar un programa en MATLAB que lleve a cabo la función de reconocimiento facial, y evaluar el funcionamiento del sistema desarrollado. Este documento se encuentra dividido en cuatro partes: INTRODUCCIÓN, MARCO TEÓRICO, IMPLEMENTACIÓN, y RESULTADOS, CONCLUSIONES Y LÍNEAS FUTURAS. En la primera parte, se hace una introducción relativa a la actualidad del reconocimiento facial y se comenta brevemente sobre las técnicas existentes para desarrollar un sistema biométrico de este tipo. En ella se justifican también aquellas técnicas que acabaron formando parte de la implementación. En la segunda parte, el marco teórico, se explica la estructura general que tiene un sistema de reconocimiento biométrico, así como sus modos de funcionamiento, y las tasas de error utilizadas para evaluar y comparar su rendimiento. Así mismo, se lleva a cabo una descripción más profunda sobre los conceptos y métodos utilizados para efectuar la detección y reconocimiento facial en la tercera parte del proyecto. La tercera parte abarca una descripción detallada de la solución propuesta. En ella se explica el diseño, características y aplicación de la implementación; que trata de un programa elaborado en MATLAB con interfaz gráfica, y que utiliza cuatro sistemas de reconocimiento facial, basados cada uno en diferentes técnicas: Análisis por componentes principales, análisis lineal discriminante, wavelets de Gabor, y emparejamiento de grafos elásticos. El programa ofrece además la capacidad de crear y editar una propia base de datos con etiquetas, dándole aplicación directa sobre el tema que se trata. Se proponen además una serie de características con el objetivo de ampliar y mejorar las funcionalidades del programa diseñado. Dentro de dichas características destaca la propuesta de un modo de verificación híbrido aplicable a cualquier rama de la biometría y un programa de evaluación capaz de medir, graficar, y comparar las configuraciones de cada uno de los sistemas de reconocimiento implementados. Otra característica destacable es la herramienta programada para la creación de grafos personalizados y generación de modelos, aplicable a reconocimiento de objetos en general. En la cuarta y última parte, se presentan al principio los resultados obtenidos. En ellos se contemplan y analizan las comparaciones entre las distintas configuraciones de los sistemas de reconocimiento implementados para diferentes bases de datos (una de ellas formada con imágenes con condiciones de adquisición no controladas). También se miden las tasas de error del modo de verificación híbrido propuesto. Finalmente, se extraen conclusiones, y se proponen líneas futuras de investigación. ABSTRACT The main goal of this project is to develop a facial recognition system. To meet this end, it was necessary to accomplish a series of specific objectives, which were: researching on the existing face recognition technics nowadays, choosing an application where face recognition might be useful, design and develop a face recognition system using MATLAB, and measure the performance of the implemented system. This document is divided into four parts: INTRODUCTION, THEORTICAL FRAMEWORK, IMPLEMENTATION, and RESULTS, CONCLUSSIONS AND FUTURE RESEARCH STUDIES. In the first part, an introduction is made in relation to facial recognition nowadays, and the techniques used to develop a biometric system of this kind. Furthermore, the techniques chosen to be part of the implementation are justified. In the second part, the general structure and the two basic modes of a biometric system are explained. The error rates used to evaluate and compare the performance of a biometric system are explained as well. Moreover, a description of the concepts and methods used to detect and recognize faces in the third part is made. The design, characteristics, and applications of the systems put into practice are explained in the third part. The implementation consists in developing a program with graphical user interface made in MATLAB. This program uses four face recognition systems, each of them based on a different technique: Principal Component Analysis (PCA), Fisher’s Linear Discriminant (FLD), Gabor wavelets, and Elastic Graph Matching (EGM). In addition, with this implementation it is possible to create and edit one´s tagged database, giving it a direct application. Also, a group of characteristics are proposed to enhance the functionalities of the program designed. Among these characteristics, three of them should be emphasized in this summary: A proposal of an hybrid verification mode of a biometric system; and an evaluation program capable of measuring, plotting curves, and comparing different configurations of each implemented recognition system; and a tool programmed to create personalized graphs and models (tagged graph associated to an image of a person), which can be used generally in object recognition. In the fourth and last part of the project, the results of the comparisons between different configurations of the systems implemented are shown for three databases (One of them created with pictures taken under non-controlled environments). The error rates of the proposed hybrid verification mode are measured as well. Finally, conclusions are extracted and future research studies are proposed.

Detección de marcas para realidad aumentada sobre dispositivos móviles

Este documento es una guía para el desarrollo de una aplicación para dispositivos móviles en Android. Dicha aplicación combina las técnicas de visión por computador para calibrar la cámara del dispositivo y localizar un elemento en el espacio en base a esos los parámetros calculados en la calibración. El diseño de la aplicación incluye las decisiones sobre la forma en que se reciben los inputs de la aplicación, que patrones se utilizan en la calibración y en la localización y como se muestran los resultados finales al usuario. También incluye un diagrama de flujo de información que representa el tránsito de esta entre los diferentes módulos. La implementación comienza con la configuración de un entorno para desarrollar aplicaciones con parte nativa en Android, después comenta el código de la aplicación paso por paso incluyendo comentarios sobre los archivos adicionales necesarios para la compilación y finalmente detalla los archivos dedicados a la interfaz. Los experimentos incluyen una breve descripción sobre cómo interpretar los resultados seguidos de una serie de imágenes tomadas de la aplicación con diferentes localizaciones del patrón. En la entrega se incluye también un video. En el capítulo de resultados y conclusiones podemos encontrar observaciones sobre el desarrollo de la práctica, opiniones sobre su utilidad, y posibles mejoras.---ABSTRACT---This document is a guide that describes the development of and application for mobile devices in Android OS. The application combines computer vision techniques to calibrate the device camera and locate an element in the real world based on the parameters of the calibration The design of the application includes the decisions over the way that the application receives its input data, the patterns used in the calibration and localization and how the results are shown to the user. It also includes a flow chart that describes how the information travels along the application modules. The development begins with the steps necessary to configure the environment to develop native Android applications, then it explains the code step by step, including commentaries on the additional files necessary to build the application and details the files of the user interface. The experiments chapter explains the way the results are shown in the experiments before showing samples of different pattern localizations. There is also a video attached. In the conclusions chapter we can find observations on the development of the TFG, opinions about its usefulness, and possibilities of improvement in the future.

Joint 4-D variational stereo reconstruction and camera calibration refinement for oceanic sea state measurements

Validating modern oceanographic theories using models produced through stereo computer vision principles has recently emerged. Space-time (4-D) models of the ocean surface may be generated by stacking a series of 3-D reconstructions independently generated for each time instant or, in a more robust manner, by simultaneously processing several snapshots coherently in a true ?4-D reconstruction.? However, the accuracy of these computer-vision-generated models is subject to the estimations of camera parameters, which may be corrupted under the influence of natural factors such as wind and vibrations. Therefore, removing the unpredictable errors of the camera parameters is necessary for an accurate reconstruction. In this paper, we propose a novel algorithm that can jointly perform a 4-D reconstruction as well as correct the camera parameter errors introduced by external factors. The technique is founded upon variational optimization methods to benefit from their numerous advantages: continuity of the estimated surface in space and time, robustness, and accuracy. The performance of the proposed algorithm is tested using synthetic data produced through computer graphics techniques, based on which the errors of the camera parameters arising from natural factors can be simulated.

Toward Visual Autonomous Ship Board Landing of a VTOL UAV

In this paper we tackle the problem of landing a helicopter autonomously on a ship deck, using as the main sensor, an on-board colour camera. To create a test-bed, we first adequately simulate the movement of a ship landing platform on the Sea, for different Sea States, for different ships, randomly and realistically enough. We use a commercial parallel robot to get this movement. Once we had this, we developed an accurate and robust computer vision system to measure the pose of the helipad with respect to the on-board camera. To deal with the noise and the possible fails of the computer vision, a state estimator was created. With all of this, we are now able to develop and test a controller that closes the loop and finish the autonomous landing task.

Optimal Piecewise Linear Function Approximation for GPU-based Applications

Many computer vision and human-computer interaction applications developed in recent years need evaluating complex and continuous mathematical functions as an essential step toward proper operation. However, rigorous evaluation of this kind of functions often implies a very high computational cost, unacceptable in real-time applications. To alleviate this problem, functions are commonly approximated by simpler piecewise-polynomial representations. Following this idea, we propose a novel, efficient, and practical technique to evaluate complex and continuous functions using a nearly optimal design of two types of piecewise linear approximations in the case of a large budget of evaluation subintervals. To this end, we develop a thorough error analysis that yields asymptotically tight bounds to accurately quantify the approximation performance of both representations. It provides an improvement upon previous error estimates and allows the user to control the trade-off between the approximation error and the number of evaluation subintervals. To guarantee real-time operation, the method is suitable for, but not limited to, an efficient implementation in modern Graphics Processing Units (GPUs), where it outperforms previous alternative approaches by exploiting the fixed-function interpolation routines present in their texture units. The proposed technique is a perfect match for any application requiring the evaluation of continuous functions, we have measured in detail its quality and efficiency on several functions, and, in particular, the Gaussian function because it is extensively used in many areas of computer vision and cybernetics, and it is expensive to evaluate.

Sistema colaborativo de detección y seguimiento de anomalías mediante visión por computador

En este proyecto se realiza el diseño e implementación de un sistema que detecta anomalías en las entradas de entornos controlados. Para ello, se hace uso de las últimas técnicas en visión por computador y se avisa visual y auditivamente, mediante un sistema hardware que recibe señales del ordenador al que está conectado. Se marca y fotografía, a una o varias personas, que cometen una infracción en las entradas de un establecimiento, vigilado con sistemas de vídeo. Las imágenes se almacenan en las carpetas correspondientes. El sistema diseñado es colaborativo, por lo tanto, las cámaras que intervienen, se comunican entre ellas a través de estructuras de datos con el objetivo de intercambiar información. Además, se utiliza conexión inalámbrica desde un dispositivo móvil para obtener una visión global del entorno desde cualquier lugar del mundo. La aplicación se desarrolla en el entorno MATLAB, que permite un tratamiento de la señal de imagen apropiado para el presente proyecto. Asimismo, se proporciona al usuario una interfaz gráfica con la que interactuar de manera sencilla, evitando así, el cambio de parámetros en la estructura interna del programa cuando se quiere variar el entorno o el tipo de adquisición de datos. El lenguaje que se escoge facilita la ejecución en distintos sistemas operativos, incluyendo Windows o iOS y, de esta manera, se proporciona flexibilidad. ABSTRACT. This project studies the design and implementation of a system that detects any anomalies on the entrances to controlled environments. To this end, it is necessary the use of last techniques in computer vision in order to notify visually and aurally, by a hardware system which receives signs from the computer it is connected to. One or more people that commit an infringement while entering into a secured environment, with video systems, are marked and photographed and those images are stored in their belonging file folder. This is a collaborative design system, therefore, every involved camera communicates among themselves through data structures with the purpose of exchanging information. Furthermore, to obtain a global environment vision from any place in the world it uses a mobile wireless connection. The application is developed in MATLAB environment because it allows an appropriate treatment of the image signal for this project. In addition, the user is given a graphical interface to easily interact, avoiding with this, changing any parameters on the program’s intern structure, when it requires modifying the environment or the data type acquisition. The chosen language eases its execution in different operating systems, including Windows or iOS, providing flexibility.

DAEDALUS at ImageCLEF 2011 Plant Identification Task: Using SIFT Keypoints for Object Detection

This paper describes the participation of DAEDALUS at ImageCLEF 2011 Plant Identification task. The task is evaluated as a supervised classification problem over 71 tree species from the French Mediterranean area used as class labels, based on visual content from scan, scan-like and natural photo images. Our approach to this task is to build a classifier based on the detection of keypoints from the images extracted using Lowe’s Scale Invariant Feature Transform (SIFT) algorithm. Although our overall classification score is very low as compared to other participant groups, the main conclusion that can be drawn is that SIFT keypoints seem to work significantly better for photos than for the other image types, so our approach may be a feasible strategy for the classification of this kind of visual content.