Face recognition

Tato práce se zabývá detekci tváře ve statickém obrazu. Teoretická část práce je zaměřena na barevné modely využívané pro detekci kůže v obraze (RGB, HSI, YCbCr), metodami využívající barevnou složku obrázků k detekci kůže (explicitní, parametrické či neparametrické metody), metrikou obrazu, detekci hran, matematickou morfologií, metodami pro klasifikaci tváře (příznakové metody, invariantní metody, znalostní metody, metody založené na porovnávání šablon). Praktická část obsahuje konkrétní návrh a praktickou realizaci dvou algoritmů detekující barvu kůže v obraze (jednoduchá metoda založená na Cr chrominační složce a statistická metoda). Praktická část také obsahuje návrh a praktickou realizaci dvou klasifikátorů tváře (příznaková metoda a metoda porovnávání šablon).This thesis is focused on face detection in static picture. Theoretical part contains color spaces (RGB, HSI, YCbCr), methods for skin detection (explicit, parametric or non-parametric methods), image metric, edge detection, mathematical morphology, methods for classification faces (appearance-based methods, feature invariant approaches, knowledge-based methods, template matching methods). Practical part of this thesis contains concept and practical realization two algorithms for segmentation skin in static image (simple method based on Cr chroma components and statistical method). Practical part contains concept and practical realization two algorithms for classification face (appearance-based method and template matching method) too.

Detecção e localização de faces em imagens

Mestrado em Engenharia Electrónica e TelecomunicaçõesDesde a existência do Homem que este utiliza a visão para detectar e localizar faces humanas para identificar e interagir com a face identificada de forma apropriada. Em visão computacional, o simples processo efectuado pelo Homem de detectar e localizar faces em imagens é bastante complexo. Nos últimos anos houve um grande aumento de pesquisa sobre a detecção e localização de faces em imagens, devido ao facto de ser o primeiro passo em qualquer sistema de reconhecimento facial. As inúmeras dificuldades que as características da face apresentam (tamanho, posição, orientação, entre outras) são um desafio para a implementação do algoritmo. Existe um grande número de técnicas para resolver esses problemas. Neste trabalho foi implementado um algoritmo para detecção e localização de faces em imagens que num primeiro passo consiste na detecção de pele, através de 3 métodos com 3 espaços de cores distintos. Posteriormente é aplicado o método do template a algumas regras geométricas baseadas no conhecimento para detectar se as diferentes zonas de pele detectadas são face ou não-face. Por ultimo é efectuada a localização da face na imagem

A vision-based approach for human hand tracking and gesture recognition.

Hand gesture interface has been becoming an active topic of human-computer interaction (HCI). The utilization of hand gestures in human-computer interface enables human operators to interact with computer environments in a natural and intuitive manner. In particular, bare hand interpretation technique frees users from cumbersome, but typically required devices in communication with computers, thus offering the ease and naturalness in HCI. Meanwhile, virtual assembly (VA) applies virtual reality (VR) techniques in mechanical assembly. It constructs computer tools to help product engineers planning, evaluating, optimizing, and verifying the assembly of mechanical systems without the need of physical objects. However, traditional devices such as keyboards and mice are no longer adequate due to their inefficiency in handling three-dimensional (3D) tasks. Special VR devices, such as data gloves, have been mandatory in VA. This thesis proposes a novel gesture-based interface for the application of VA. It develops a hybrid approach to incorporate an appearance-based hand localization technique with a skin tone filter in support of gesture recognition and hand tracking in the 3D space. With this interface, bare hands become a convenient substitution of special VR devices. Experiment results demonstrate the flexibility and robustness introduced by the proposed method to HCI.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L8. Source: Masters Abstracts International, Volume: 43-03, page: 0883. Adviser: Xiaobu Yuan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Enhancing person annotation for personal photo management using content and context based technologies

Rapid technological growth and the decreasing cost of photo capture means that we are all taking more digital photographs than ever before. However, lack of technology for automatically organising personal photo archives has resulted in many users left with poorly annotated photos, causing them great frustration when such photo collections are to be browsed or searched at a later time. As a result, there has recently been significant research interest in technologies for supporting effective annotation. This thesis addresses an important sub-problem of the broad annotation problem, namely "person annotation" associated with personal digital photo management. Solutions to this problem are provided using content analysis tools in combination with context data within the experimental photo management framework, called “MediAssist”. Readily available image metadata, such as location and date/time, are captured from digital cameras with in-built GPS functionality, and thus provide knowledge about when and where the photos were taken. Such information is then used to identify the "real-world" events corresponding to certain activities in the photo capture process. The problem of enabling effective person annotation is formulated in such a way that both "within-event" and "cross-event" relationships of persons' appearances are captured. The research reported in the thesis is built upon a firm foundation of content-based analysis technologies, namely face detection, face recognition, and body-patch matching together with data fusion. Two annotation models are investigated in this thesis, namely progressive and non-progressive. The effectiveness of each model is evaluated against varying proportions of initial annotation, and the type of initial annotation based on individual and combined face, body-patch and person-context information sources. The results reported in the thesis strongly validate the use of multiple information sources for person annotation whilst emphasising the advantage of event-based photo analysis in real-life photo management systems

Performance evaluation of single and multiple-Gaussian models for skin color modeling

We present an experimental setup to evaluate the relative performance of single Gaussian models and Gaussian mixture models for skin color modeling. Firstly, a sample set of 1,120,000 skin pixels from a number of ethnic groups is selected and represented in the chromaticity space. Parameter estimation for both the single Gaussian and seven (with 2 to 8 Gaussian components) Gaussian mixture models is performed. For the mixture models, learning is carried out via the expectation-maximisation (EM) algorithm. In order to compare performances achieved by the 8 different models, we apply to each model a test set of 800 images-none from the training set. True skin regions, representing ground truth, are manually selected, and false positive and true positive rates are computed for each value of a specific threshold. Finally, receiver operating characteristics (ROC) curves are plotted for each model, making it possible to analyze and compare their relative performances. Results obtained show that, for medium to high true positive rates, mixture models (with 2 to 8 components) outperform the single Gaussian model. Nevertheless, for low false positive rates, all the models behave similarly