How to efficiently and relistically generate 3D human face models is a very interesting and difficult problem in computer graphics. animated face models are essential to computer games, films making, online chat, virtual presence, video conferencing, etc. As the progress of computer technology, people request for more and more multimedia effects. Therefore, construct 3D human face models and facial animation are enthusiastically investigated in recent years. There are many kinds of method that used to construct 3D human face models. Such as laser scanners and computer graphics. So far, the most popular commercially available tools have utilized laser scanners. But it is not able to trace moving object. We bring up a technique that construct 3D human face model based on real image sequence. The full procedure can be divided into 4 parts. In the first step we use two cameras take picture con human face simultaneously. By the distance within two cameras we can calculate the depth of human face and build up a 3D face model. The second step is aimed at one image sequence which is taken by the same camera. By comparing the feature poins on previous image afterward image we can get the motion vector of human face. Now we can construct a template of animated 3D face model. After that we can map any kind of 2D new character image into the template, then build new character's animation. The full procedure is automatic. We can construct exquisite human facial animation easily.

Computer interfaces has changed to 3D graphics environments due to its high number of applications ranging from scientific importance to entertainment. To enhance the realism of the 3D models, an established rendering technique, texture mapping, is used. In computer vision, a way to generate this texture is to combine extracted parts of multiple images of real objects and it is the topic studied in this thesis. While the 3D face model is obtained by using 3D scanner, the texture to cover the model is constructed from multiple images. After marking control points on images and also on 3D face model, a texture image to cover the 3D face model is generated. Moreover, effects of the some features of OpenGL, a graphical library, on 3D texture covered face model are studied.

Esta dissertação apresenta um novo método para cálculo da pose da cabeça em imagens monoculares. Este cálculo é estimado no sistema de coordenadas da câmera, comparando as posições das características faciais específicas com as de múltiplas instâncias do modelo da face em 3D. Dada uma imagem de uma face humana, o método localiza inicialmente as características faciais, como nariz, olhos e boca. Estas últimas são detectadas e localizadas através de um modelo ativo de forma para faces. O algoritmo foi treinado sobre um conjunto de dados com diferentes poses de cabeça. Para cada face, obtemos um conjunto de pontos característicos no espaço de imagem 2D. Esses pontos são usados como referências na comparação com os respectivos pontos principais das múltiplas instâncias do nosso modelo de face em 3D projetado no espaço da imagem. Para obter a profundidade de cada ponto, usamos as restrições impostas pelo modelo 3D da face por exemplo, os olhos tem uma determinada profundidade em relação ao nariz. A pose da cabeça é estimada, minimizando o erro de comparação entre os pontos localizados numa instância do modelo 3D da face e os localizados na imagem. Nossos resultados preliminares são encorajadores e indicam que a nossa abordagem produz resultados mais precisos que os métodos disponíveis na literatura. / This dissertation presents a new method to accurately compute the head pose in mono cular images. The head pose is estimated in the camera coordinate system, by comparing the positions of specific facial features with the positions of these facial features in multiple instances of a prior 3D face model. Given an image containing a face, our method initially locates some facial features, such as nose, eyes, and mouth; these features are detected and located using an Adaptive Shape Model for faces , this algorithm was trained using on a data set with a variety of head poses. For each face, we obtain a collection of feature locations (i.e. points) in the 2D image space. These 2D feature locations are then used as references in the comparison with the respective feature locations of multiple instances of our 3D face model, projected on the same 2D image space. To obtain the depth of every feature point, we use the 3D spatial constraints imposed by our face model (i.e. eyes are at a certain depth with respect to the nose, and so on). The head pose is estimated by minimizing the comparison error between the 3D feature locations of the face in the image and a given instance of the face model (i.e. a geometrical transformation of the face model in the 3D camera space). Our preliminary experimental results are encouraging, and indicate that our approach can provide more accurate results than comparable methods available in the literature.

Esta dissertação apresenta um novo método para cálculo da pose da cabeça em imagens monoculares. Este cálculo é estimado no sistema de coordenadas da câmera, comparando as posições das características faciais específicas com as de múltiplas instâncias do modelo da face em 3D. Dada uma imagem de uma face humana, o método localiza inicialmente as características faciais, como nariz, olhos e boca. Estas últimas são detectadas e localizadas através de um modelo ativo de forma para faces. O algoritmo foi treinado sobre um conjunto de dados com diferentes poses de cabeça. Para cada face, obtemos um conjunto de pontos característicos no espaço de imagem 2D. Esses pontos são usados como referências na comparação com os respectivos pontos principais das múltiplas instâncias do nosso modelo de face em 3D projetado no espaço da imagem. Para obter a profundidade de cada ponto, usamos as restrições impostas pelo modelo 3D da face por exemplo, os olhos tem uma determinada profundidade em relação ao nariz. A pose da cabeça é estimada, minimizando o erro de comparação entre os pontos localizados numa instância do modelo 3D da face e os localizados na imagem. Nossos resultados preliminares são encorajadores e indicam que a nossa abordagem produz resultados mais precisos que os métodos disponíveis na literatura. / This dissertation presents a new method to accurately compute the head pose in mono cular images. The head pose is estimated in the camera coordinate system, by comparing the positions of specific facial features with the positions of these facial features in multiple instances of a prior 3D face model. Given an image containing a face, our method initially locates some facial features, such as nose, eyes, and mouth; these features are detected and located using an Adaptive Shape Model for faces , this algorithm was trained using on a data set with a variety of head poses. For each face, we obtain a collection of feature locations (i.e. points) in the 2D image space. These 2D feature locations are then used as references in the comparison with the respective feature locations of multiple instances of our 3D face model, projected on the same 2D image space. To obtain the depth of every feature point, we use the 3D spatial constraints imposed by our face model (i.e. eyes are at a certain depth with respect to the nose, and so on). The head pose is estimated by minimizing the comparison error between the 3D feature locations of the face in the image and a given instance of the face model (i.e. a geometrical transformation of the face model in the 3D camera space). Our preliminary experimental results are encouraging, and indicate that our approach can provide more accurate results than comparable methods available in the literature.

Esta dissertação apresenta um novo método para cálculo da pose da cabeça em imagens monoculares. Este cálculo é estimado no sistema de coordenadas da câmera, comparando as posições das características faciais específicas com as de múltiplas instâncias do modelo da face em 3D. Dada uma imagem de uma face humana, o método localiza inicialmente as características faciais, como nariz, olhos e boca. Estas últimas são detectadas e localizadas através de um modelo ativo de forma para faces. O algoritmo foi treinado sobre um conjunto de dados com diferentes poses de cabeça. Para cada face, obtemos um conjunto de pontos característicos no espaço de imagem 2D. Esses pontos são usados como referências na comparação com os respectivos pontos principais das múltiplas instâncias do nosso modelo de face em 3D projetado no espaço da imagem. Para obter a profundidade de cada ponto, usamos as restrições impostas pelo modelo 3D da face por exemplo, os olhos tem uma determinada profundidade em relação ao nariz. A pose da cabeça é estimada, minimizando o erro de comparação entre os pontos localizados numa instância do modelo 3D da face e os localizados na imagem. Nossos resultados preliminares são encorajadores e indicam que a nossa abordagem produz resultados mais precisos que os métodos disponíveis na literatura. / This dissertation presents a new method to accurately compute the head pose in mono cular images. The head pose is estimated in the camera coordinate system, by comparing the positions of specific facial features with the positions of these facial features in multiple instances of a prior 3D face model. Given an image containing a face, our method initially locates some facial features, such as nose, eyes, and mouth; these features are detected and located using an Adaptive Shape Model for faces , this algorithm was trained using on a data set with a variety of head poses. For each face, we obtain a collection of feature locations (i.e. points) in the 2D image space. These 2D feature locations are then used as references in the comparison with the respective feature locations of multiple instances of our 3D face model, projected on the same 2D image space. To obtain the depth of every feature point, we use the 3D spatial constraints imposed by our face model (i.e. eyes are at a certain depth with respect to the nose, and so on). The head pose is estimated by minimizing the comparison error between the 3D feature locations of the face in the image and a given instance of the face model (i.e. a geometrical transformation of the face model in the 3D camera space). Our preliminary experimental results are encouraging, and indicate that our approach can provide more accurate results than comparable methods available in the literature.

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This thesis is about biometric 3D face recognition . A general biometric system as well as functioning of biometric system are present . Techniques used in 2D and 3D face recognition are described . Finally , an automatic biometric system for 3D face recognition is proposed and implemeted . This system divide face for areas by position of detected landmarks . Particular areas are compared separately . Final system fusion results from Eigenfaces and ARENA algorithms .