O processamento e a identificação de emoções faciais constituem ações essenciais para estabelecer interação entre pessoas. Alguns transtornos psiquiátricos podem limitar a capacidade de um indivíduo em reconhecer emoções em expressões faciais. De modo a contribuir com a solução deste problema, técnicas computacionais podem ser utilizadas para compor ferramentas destinadas ao diagnóstico, avaliação e treinamento no reconhecimento de tais expressões. Com esta motivação, o objetivo deste trabalho é definir, implementar e avaliar um método para sintetizar expressões faciais que representam emoções em imagens de pessoas reais. Nos trabalhos encontrados na literatura a principal ideia é que a expressão facial da imagem de uma pessoa pode ser reconstituída na imagem de outra pessoa. Este estudo difere-se das abordagens apresentadas na literatura ao propor uma técnica que considera a similaridade entre imagens faciais para escolher aquela que será empregada como origem para a reconstituição. Desta maneira, pretende-se aumentar o realismo das imagens sintetizadas. A abordagem sugerida para resolver o problema, além de buscar as faces mais similares em banco de imagens, faz a deformação dos componentes faciais e o mapeamento das diferenças de iluminação na imagem destino. O realismo das imagens geradas foi mensurado de forma objetiva e subjetiva usando imagens disponíveis em bancos de imagens públicos. Uma análise visual mostrou que as imagens sintetizadas com base em faces similares apresentaram um grau de realismo adequado, principalmente quando comparadas com imagens sintetizadas a partir de faces aleatórias. Além de constituir uma contribuição para a geração de imagens a serem aplicadas em ferramentas de auxílio ao diagnóstico e terapia de distúrbios psiquiátricos, oferece uma contribuição para a área de Ciência da Computação, por meio da proposição de novas técnicas de síntese de expressões faciais / The ability to process and identify facial emotions are essential factors for an individual\'s social interaction. Some psychiatric disorders can limit an individual\'s ability to recognize emotions in facial expressions. This problem could be confronted by using computational techniques in order to develop learning environments for diagnosis, evaluation, and training in identifying facial emotions. With this motivation, the objective of this work is to define, implement and evaluate a method to synthesize realistic facial expression that represents emotions in images of real people. The main idea of the studies found in the literature is that a facial expression of one persons image can be reenacted in an another persons image. The study differs from the approaches presented in the literature when proposing a technique that considers the similarity between facial images to choose the one that will be used as the origin for reenactment. As a result, we intend to increase the realism of the synthesized images. Our approach to solve the problem, besides searching for the most similar facial components in the image dataset, also deforms the facial elements and maps the differences of illumination in the target image. A visual analysis showed that the images synthesized on the basis of similar faces presented an adequate degree of realism, especially when compared with images synthesized from random faces. The study will contribute to the generation of the images applied to tools for the diagnosis and therapy of psychiatric disorders, and also contribute to the computational field, through the proposition of new techniques for facial expression synthesis

Approximating the facial features for forensic facial approximation is challenging, especially the nose. Numerous methods have been published to position the tip of the nose in profile with variable results. Gerasimov’s two-tangent method is the most commonly used. However, a recent article published by Ullrich and Stephan (2011) states that the method was not properly performed and provides new guidelines. This research used a sample of CT scans from a Denmark population (N=66) to determined which of Gerasimov’s literal translation or Ullrich and Stephan’s (2011) new version of the two-tangent method is the most accurate. A combination of the two methods was also evaluated to determine the effect of each tangent independently, and the effect of intraobserver error. It was determine that the new guidelines result in smaller mean difference but no method can accurately position the tip of the nose due to the lack of experience from the practitioner.

Approximating the facial features for forensic facial approximation is challenging, especially the nose. Numerous methods have been published to position the tip of the nose in profile with variable results. Gerasimov’s two-tangent method is the most commonly used. However, a recent article published by Ullrich and Stephan (2011) states that the method was not properly performed and provides new guidelines. This research used a sample of CT scans from a Denmark population (N=66) to determined which of Gerasimov’s literal translation or Ullrich and Stephan’s (2011) new version of the two-tangent method is the most accurate. A combination of the two methods was also evaluated to determine the effect of each tangent independently, and the effect of intraobserver error. It was determine that the new guidelines result in smaller mean difference but no method can accurately position the tip of the nose due to the lack of experience from the practitioner.

Diss. (sammanfattning) Linköping : Univ., 2002. / Härtill 5 uppsatser.

Thesis (M. Sc.)--University of Alberta, 1998. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (M.S.)--University of Michigan, 1960.

Thesis (M.S.)--University of Michigan, 1954.

Thesis (M.S.)--University of Michigan, 1955.

Thesis (Ph. D.)--Eberhard Karls-Universität, 1933.

Thesis (M. Sc.)--University of Alberta, 1998. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.