Facial expressions are a useful source of information about the emotional state of others. However, facial expressions do not always correspond with an underlying emotional state. It is advantageous for perceivers to be able to differentiate between those expressions that are associated with a corresponding emotional state (genuine expressions) and those which are not associated with underlying emotions (posed expressions). The present study investigated the sensitivity of children with Autism Spectrum Disorder (ASD), and age and sex-matched control children to the different emotions underlying posed and genuine smiles. The first task required participants to listen to 12 emotion eliciting stories and select, from a grid of 4 facial expressions (a genuine smile, a posed smile, a neutral expression and a sad expression) that which matched how the target in the story would feel. Children with ASD correctly matched facial expressions and stories than did participants without ASD. The second task required children to look at a series of faces, each displaying either a posed smile, a genuine smile or a neutral expression and indicate whether each target was or was not happy. Participants with ASD were less sensitive both to the underlying emotional state of the targets and to the difference between posed and genuine smiles than were the control participants. Results are discussed in terms of the social deficits symptomatic of ASD.

Chapter One introduces the field of evolutionary psychology as well as provides a review of factors influencing facial attractiveness. Chapter Two presents empirical evidence that online studies may provide a distorted perspective on cross-cultural face preferences since online samples are not representative of the populations in developing countries. In El Salvador, participants without internet access preferred more feminine men as well as heavier and more masculine women when compared to participants with internet access. One possible explanation for such findings is that the level of harshness in the environment may be influencing preferences. One individual difference that is influenced by environmental harshness is age of menarche. Chapter Three thus provides exploratory evidence that age of menarche also influences masculinity preferences. Chapter Four further examines this environmental harshness hypothesis by repeatedly testing students undergoing intensive training at an army camp. Increases in the harshness of the environment led to an increased male attraction to cues of higher weight in female faces. Such changes in preferences may be adaptive because they allow for more opportunities to form partnerships with individuals who are better equipped to survive. An alternative explanation for the empirical findings in Chapters Two and Four is that familiarity may also influence preferences. Chapter Five tests this familiarity hypothesis by examining the faces of participants in different areas of El Salvador and Malaysia. Rural participants preferred heavier female faces than urban participants. Additionally, the faces of female participants from rural areas were rated as looking heavier. This finding suggests that familiarity may indeed influence attractiveness perceptions. Lastly, Chapter Six draws conclusions from the empirical findings reported in Chapters Two-Five and lists proposals of future research that could further enhance our understanding of what we find attractive.

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[pt] A presente dissertação estudo dois problemas de detecção de padrões em imagens com fundo complexo, casos onde os algoritmos de segmentação convencionais não podem proporcionar bons resultados: a localização de Unidades Estruturais (UE`s) em imagens obtidas por Microscópio Eletrônico de Transmissão em Alta Resolução, e a detecção de faces frontais na posição vertical em imagens. Apesar de serem abordados problemas diferentes, as metodologias empregadas na solução de ambos os problemas possuem semelhanças. Uma operação de vizinhança é aplicada a imagem de entrada em busca de padrões de interesse. Sendo cada região extraída desta imagem submetida a um operador matemático composto por etapas de pré-processamento, redução de dimensionalidade e classificação. Na detecção de UE`s foram empregados três métodos distintos de redução de dimensionalidade - Análise de Componentes Principais (PCA), PCA do conjunto de treinamento equilibrado (PCAEq), e um método inédito, eixos que maximizam a distância ao centróide de uma classe (MAXDIST) - e dois modelos de classificador - classificador baseado na distância euclideana (EUC) e rede neural back-propagation (RN). A combinação PCAEq/RN forneceu taxa de detecção de 88% para 25 componentes. Já a combinação MAXDIST/EUC com apenas uma atributo forneceu 82% de detecção com menos falsas detecções. Na detecção de faces foi empregada uma nova abordagem, que utiliza uma rede neural back-propagation como classificador. Aplica-se a sua entrada recebe a representação no subespaço das faces e o erro de reconstrução. Em comparação com os resultados de referência da literatura na área, o método proposto atingiu taxas de detecção similares. / [en] This dissertation studies two pattern detection problems in images with complex background, in which standard segmentation techniques do not provide good results: the detection of structural units (SU`s) in images obtained through High resolution transmission Electron Microscopy and the detection of frontal human faces in images. The methods employed in the solution of both problems have many similarities - a neighborhood operator, basically composed of pre-processing, dimensionality reduction and classification steps, scans the input image searching for the patterns of interest. For SU detection three dimensionality reduction methods - Principal Component Analysis (PCA), PCA of the balanced training set (PACEq), and a new method, axis that maximize the distance to a given class centroid (MAXDIST) -, and two classifiers - Euclidean Distance (EUC) and back-propagation neural network (RN). The MAXDIST/EUC combination, with just one component, provided a detection rate of 82% with less false detections. For face detection a new approach was employed, using a back-propagation neural network as classifier. It takes as input a representation in the so-called face space and the reconstruction error (DFFS). In comparison with benchmark results from the literature, the proposed method reached similar detection rates.

[pt] O rastreamento visual é uma etapa essencial em diversas aplicações de visão computacional. Em particular, o rastreamento facial é considerado uma tarefa desafiadora devido às variações na aparência da face, devidas à etnia, gênero, presença de bigode ou barba e cosméticos, além de variações na aparência ao longo da sequência de vídeo, como deformações, variações em iluminação, movimentos abruptos e oclusões. Geralmente, os rastreadores são robustos a alguns destes fatores, porém não alcançam resultados satisfatórios ao lidar com múltiplos fatores ao mesmo tempo. Uma alternativa é combinar as respostas de diferentes rastreadores para alcançar resultados mais robustos. Este trabalho se insere neste contexto e propõe um novo método para a fusão de rastreadores escalável, robusto, preciso e capaz de manipular rastreadores independentemente de seus modelos. O método prevê ainda a integração de detectores de faces ao modelo de fusão de forma a aumentar a acurácia do rastreamento. O método proposto foi implementado para fins de validação, tendo sido testado em diversas configurações que combinaram até cinco rastreadores distintos e um detector de faces. Em testes realizados a partir de quatro sequências de vídeo que apresentam condições diversas de imageamento o método superou em acurácia os rastreadores utilizados individualmente. / [en] Visual tracking is fundamental in several computer vision applications. In particular, face tracking is challenging because of the variations in facial appearance, due to age, ethnicity, gender, facial hair, and cosmetics, as well as appearance variations in long video sequences caused by facial deformations, lighting conditions, abrupt movements, and occlusions. Generally, trackers are robust to some of these factors but do not achieve satisfactory results when dealing with combined occurrences. An alternative is to combine the results of different trackers to achieve more robust outcomes. This work fits into this context and proposes a new method for scalable, robust and accurate tracker fusion able to combine trackers regardless of their models. The method further provides the integration of face detectors into the fusion model to increase the tracking accuracy. The proposed method was implemented for validation purposes and was tested in different configurations that combined up to five different trackers and one face detector. In tests on four video sequences that present different imaging conditions the method outperformed the trackers used individually.

The thesis illustrates the development of DSP-based ¡§Relativity Gene Algorithm For Multiple Faces Recognition System＂. The recognition system is divided into three systems: Ellipsoid location system of multiple human faces, Feature points and feature vectors extraction system, Recognition system algorithm of multiple human faces. Ellipsoid location system of multiple human faces is using CCD camera or digital camera to capture image data which will be recognized in any background, and transmitting the image data to SRAM on DSP through the PPI interface on DSP. Then, using relatively genetic algorithm with the face color of skin and ellipsoid information locate face ellipses which are any location and size in complex background. Feature points and feature vectors extraction system finds facial feature points in located human face by many image process skills. Recognition system algorithm of multiple human faces is using decision by majority. Using characteristic vectors compares every vector in the database. Then, we draw out the highest ID. The recognizable result is over. The experimental result of the developed recognition system demonstrates satisfied and efficiency.

The current thesis explored the intentional forgetting of different types of facial expression (Angry, Neutral, Happy) within the item-method directed forgetting paradigm (Experiments 1-4). Also, as a manipulation check, Experiment 5 obtained the subjective ratings of valence and arousal for the different types of facial expression used in the previous four Experiments. In summary, a significant directed forgetting effect occurred for Neutral facial expressions; however, a significant directed forgetting effect did not consistently occur for emotional facial expressions (e.g., there was no directed forgetting effect for Angry facial expressions in Experiments 2 and 3, or Happy facial expressions in Experiment 3). These findings are discussed in terms of encoding time as well as valence and arousal, and how these two factors modulate the effect of emotional facial expression on the ability to intentionally forget.

The present study investigates the intersection of working memory and emotion recognition in young adults with autism spectrum disorders (ASD) and neurotypical controls. The executive functioning theory of autism grounds key impairments within the cognitive realm, whereas social-cognitive theories view social functioning impairments as primary. Executive functioning theory of ASD has been criticized because executive functioning is too broad and is composed of separable, component skills. In the current study, the focus is narrowed to one of those components, working memory. It has been suggested that executive functioning may play a role in effective social interactions. Emotion recognition is an important aspect of social reciprocity, which is impaired in ASD. The current study investigates this hypothesis by combining working memory and emotion recognition into a single task, the n-back, as a model of social interaction and comparing performance between adults with ASD and controls. A validates set of facial expression stimuli (NimStim) was modified to remove all extraneous detail, and type of emotion was tightly controlled across 1-, 2-, and 3-back conditions. Results include significantly lower accuracy in each of the working memory load conditions in the ASD group compared to the control group, as well as in a baseline, maintenance memory task. The control group's reaction time increased as working memory load increased, whereas the ASD group's reaction time did not significantly vary by n-back level. The pattern of results suggests that the limit for n-back with emotional expressions is 2-back, due to near chance level performance in both groups for 3-back, as well as definitive problems in short term memory for facial expressions of emotion in high-functioning individuals with ASD, in contrast to previous findings of near perfect short term memory for facial expressions of emotion in controls.

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Made available in DSpace on 2014-06-12T15:54:48Z (GMT). No. of bitstreams: 2 arquivo7603_1.pdf: 2766072 bytes, checksum: 45006307abb4b9f20d8348671c07565d (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2011 / A interação natural diz respeito à forma natural como as pessoas se comunicam, seja através de gestos, expressões e movimentos. Pesquisas nessa área tentam construir sistemas que possam compreender essas ações. Sistemas baseados em interação natural são uma tecnologia não intrusiva, a qual não é notada pelo usuário no cotidiano e tem um bom tempo de resposta de processamento. As interfaces atuais tentam se aproximar cada vez mais das perspectivas humanas, sendo ainda limitadas por tecnologias de entrada de dados não adequadas. Dentro deste contexto se encontram as técnicas de realidade aumentada sem marcadores (MAR), que realizam o rastreamento e o registro de objetos virtuais em cenas reais sem a utilização de elementos intrusivos às cenas, fator que possibilita sua utilização em ambientes pouco controlados e tornam sua definição mais complexa. Um ramo de aplicação em destaque nos meios acadêmico e industrial é o rastreamento de faces tanto do ponto de vista de aplicações de MAR quanto de sistemas de segurança, devido à possibilidade de facilitar o reconhecimento automático de faces em cenários de tempo real. A capacidade de estimar a pose da cabeça de outra pessoa é uma habilidade humana comum, mas que representa um desafio para os sistemas de visão computacional. Um rastreador de posição de face ideal deve ser invariante a rotação e escala, ser robusto, inicializar automaticamente, suportar oclusão parcial e total, além de mudança de iluminação e movimentos de cabeça rápidos. Neste trabalho desenvolvemos um sistema de rastreamento de face interativo que utiliza técnicas 2D, uma câmera e características naturais da cena para se obter um rastreamento que contenha as requisições necessárias por um estimador de face ideal. O algoritmo utilizado para o rastreamento de face de longo prazo integrou duas técnicas para obtenção de uma aplicação robusta e em tempo real: algoritmo de Haar e TLD (tracking learnig detect), sendo que o primeiro é responsável pela inicialização automática da face no ambiente, enquanto o segundo utiliza técnicas de aprendizado supervisionado, usando os próprios erros para aprimorar o rastreamento