Robust Face Recognition for Data Mining

While the technology for mining text documents in large databases could be said to be relatively mature, the same cannot be said for mining other important data types such as speech, music, images and video. Yet these forms of multimedia data are becoming increasingly prevalent on the internet and intranets as bandwidth rapidly increases due to continuing advances in computing hardware and consumer demand. An emerging major problem is the lack of accurate and efficient tools to query these multimedia data directly, so we are usually forced to rely on available metadata such as manual labeling. Currently the most effective way to label data to allow for searching of multimedia archives is for humans to physically review the material. This is already uneconomic or, in an increasing number of application areas, quite impossible because these data are being collected much faster than any group of humans could meaningfully label them - and the pace is accelerating, forming a veritable explosion of non-text data. Some driver applications are emerging from heightened security demands in the 21st century, postproduction of digital interactive television, and the recent deployment of a planetary sensor network overlaid on the internet backbone

Faces and eyes Detection in Digital Images Using Cascade Classifiers

In this Article we present a way to implementation and detect the face and eyes  on digital image, based on Haar-like features extraction and cascade classifier, these techniques used in 100 % and 92% for faces and eyes detection respectively for the best all cases using low processing time , we used cheap equipment in our work (Acer TravelMate web camera ) . OpenCV library(computer vision library) and Python language used in this work

Desarrollo de software para la detección de personas por medio de sensor RGBD

Se desea implementar en un entorno robotizado la manera de detectar personas para el control de un robot y mantener la seguridad de las personas que puedan entrar en el volumen de trabajo de este. Para ello fue necesario la comunicación con un sensor de profundidad y el entrenamiento de clasificadores. Se analizaron librerías para la comunicación de Java con el sensor de profundidad Kinect tales como: J4k y processing mirando su facilidad de modificación o mantenimiento, documentación y finalmente que tanto se podían intervenir estas librerías. Se encontró que processing tiene mayor documentación, facilidad en implementación entre otros aspectos que favorecieron a que se desarrollara el software con esta herramienta y se dejara de lado la librería J4k. Para la parte de detección de personas se utilizó un clasificador en cascada de la librería de Opencv, para lograr nuestro cometido fue necesario entrenar nuestro clasificador con bases de datos descargadas para las pruebas iniciales, pero posteriormente se desarrollara una base de datos propia para aumentar el grado de certeza en el entorno que se hará la implementación. Y se desarrolló o implemento un algoritmo para analizar otros clarificadores por medio de balu y matlab para saber que clasificador es más óptimo para en la siguiente etapa del proyecto saber con qué clasificador es más eficaz con las imágenes RGBD.Ingeniero de Sistemaspregrad

A survey of face detection, extraction and recognition

The goal of this paper is to present a critical survey of existing literatures on human face recognition over the last 4-5 years. Interest and research activities in face recognition have increased significantly over the past few years, especially after the American airliner tragedy on September 11 in 2001. While this growth largely is driven by growing application demands, such as static matching of controlled photographs as in mug shots matching, credit card verification to surveillance video images, identification for law enforcement and authentication for banking and security system access, advances in signal analysis techniques, such as wavelets and neural networks, are also important catalysts. As the number of proposed techniques increases, survey and evaluation becomes important

Merging Augmented Reality with Television Shows to Enhance the Viewer Experience

Nowadays, television no longer has the same effect on viewers as it had decades ago. The “traditional” television has been losing audience over the years in favor of new technologies. The time that was formerly spent watching televi-sion, was replaced by smartphones and tablets, where the viewer has the oppor-tunity to interact with the content that is provided to him, receiving stimuli that television cannot offer on its own. More and more people are looking for new ways to socialize and interact outside the space they are confined to, in order to discuss certain topics and watch videos, or images published by others. This makes the concept of watching television, just for the pleasure of watching, an old-fashioned concept that needs to be adapted to the modern times. This thesis aims to introduce innovative concepts of interactivity in television contexts, and to achieve it, we will explore the possibility of integrating augmented reality (AR) concepts with television shows to enhance the viewer experience. By using AR, we can view objects and information that otherwise would not be possible, simply because they do not exist in our reality or the original movie. This technology is earning an important role in our day-to-day activities, namely in the entertain-ment area. Our goal is to allow viewers to watch and interact with TV shows through a mobile device and use AR elements to present important information and amusing effects by overlaying the video content. With this approach, we hope to introduce a new way of interacting with TV shows so that we can meet the expectations of a new generation of audiences. Taking into account the results we had, this concept can be considered a success and can possibly be one of the next steps in TV show user interaction