Head-Tracking Haptic Computer Interface for the Blind

In today’s heavily technology-dependent society, blind and visually impaired people are becoming increasingly disadvantaged in terms of access to media, information, electronic commerce, communications and social networks. Not only are computers becoming more widely used in general, but their dependence on visual output is increasing, extending the technology further out of reach for those without sight. For example, blindness was less of an obstacle for programmers when command-line interfaces were more commonplace, but with the introduction of Graphical User Interfaces (GUIs) for both development and final applications, many blind programmers were made redundant (Alexander, 1998; Siegfried et al., 2004). Not only are images, video and animation heavily entrenched in today’s interfaces, but the visual layout of the interfaces themselves hold important information which is inaccessible to sightless users with existing accessibility technology

Facial Geometry Identification through Fuzzy Patterns with RGBD Sensor

Automatic human facial recognition is an important and complicated task; it is necessary to design algorithms capable of recognizing the constant patterns in the face and to use computing resources efficiently. In this paper we present a novel algorithm to recognize the human face in real time; the systems input is the depth and color data from the Microsoft KinectTM device. The algorithm recognizes patterns/shapes on the point cloud topography. The template of the face is based in facial geometry; the forensic theory classifies the human face with respect to constant patterns: cephalometric points, lines, and areas of the face. The topography, relative position, and symmetry are directly related to the craniometric points. The similarity between a point cloud cluster and a pattern description is measured by a fuzzy pattern theory algorithm. The face identification is composed by two phases: the first phase calculates the face pattern hypothesis of the facial points, configures each point shape, the related location in the areas, and lines of the face. Then, in the second phase, the algorithm performs a search on these face point configurations

Mathematical Camera Array Optimization for Face 3D Modeling Application

Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability needed for image-based 3D face models. This is caused among many factors by the hardware limitations and imperfection of the cameras and the lack of proficiency in designing the ideal camera-system configuration. Accordingly, for precise measurements, we still need engineering-based techniques to ascertain the specific level of deliverables quality. In this paper, an optimal geometric design methodology of the camera network is presented by investigating different multi-camera system configurations composed of four up to eight cameras. A mathematical nonlinear constrained optimization technique is applied to solve the problem and each camera system configuration is tested for a facial 3D model where a quality assessment is applied to conclude the best configuration. The optimal configuration is found to be a 7-camera array, comprising a pentagon shape enclosing two additional cameras, offering high accuracy. For those who prioritize point density, a 9-camera array with a pentagon and quadrilateral arrangement in the X-Z plane is a viable choice. However, a 5-camera array offers a balance between accuracy and the number of cameras

4D Unconstrained Real-time Face Recognition Using a Commodity Depthh Camera

Robust unconstrained real-time face recognition still remains a challenge today. The recent addition to the market of lightweight commodity depth sensors brings new possibilities for human-machine interaction and therefore face recognition. This article accompanies the reader through a succinct survey of the current literature on face recognition in general and 3D face recognition using depth sensors in particular. Consequent to the assessment of experiments performed using implementations of the most established algorithms, it can be concluded that the majority are biased towards qualitative performance and are lacking in speed. A novel method which uses noisy data from such a commodity sensor to build dynamic internal representations of faces is proposed. Distances to a surface normal to the face are measured in real-time and used as input to a specific type of recurrent neural network, namely long short-term memory. This enables the prediction of facial structure in linear time and also increases robustness towards partial occlusions

RGB-D SLAM

This project has been developed as an implementation of a SLAM technique called GraphSLAM. This technique applies the theory of graphs to create an on-line optimization system that allows robots to map the scenario and locate themselves using a Time of Flight camera as the input source. To do that, a RGB-D system has been calibrated and used to create color 3D point clouds. With this information, the feature detector module estimates, as a first approximation, the pose of the camera. Therefore, a ICP pose refinement completes the graph structure. Finally, a HogMAN graph optimizer close the loop on each iteration using a hierarchical manifold optimization. As a result, 3D color maps are created containing, at the same time, the exact position of the robot over the map. ____________________________________________________________________________________________________________________________________Este proyecto ha sido desarrollado como una propuesta para implementación de una de las técnicas de SLAM denominada GraphSLAM. Esta técnica aplica la teoría de grafos para crear un sistema de optimización en tiempo real que permite a los robots mapear un escenario y localizarse utilizando una cámara de tiempo de vuelo como fuente de información. Para llevarlo a cabo, ha sido desarrollado y calibrado un sistema RGB-D que tiene como finalidad crear una nube de puntos 3D. Con esta información, el detector de características estima, como primera aproximación, la posición de la cámara. A continuación, mediante ICP se realiza una corrección más fina de la estructura del grafo. Finalmente, mediante un optimizador global de grafos denominado HogMAN se cierra el bucle en cada iteración basándose en manifolds jerárquicos. Como resultado, se generan mapas 3D a color que contien, al mismo tiempo, la posición exacta del robot dentro del mapa.Ingeniería Técnica en Telemátic