Thesis (Ph. D.)--University of California, San Diego, 2005. / Title from first page of PDF file (viewed March 1, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references ( p. 113-116).

Thesis (Ph. D.)--University of California, San Diego and San Diego State University, 2007. / Title from first page of PDF file (viewed June 4, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1987. / Vita. Bibliography: leaves [115]-121.

Thesis (M.S.)--Villanova University, 2007. / Psychology Dept. Includes bibliographical references.

Crucial to human interaction and development, emotions have long fascinated psychologists. Current thinking suggests that specific emotions, regardless of the channel in which they are communicated, are processed by separable neural mechanisms. Yet much research has focused only on the interpretation of facial expressions of emotion. The present research addressed this oversight by exploring recognition of emotion from facial, vocal, and gestural tasks. Happiness and disgust were best conveyed by the face, yet other emotions were equally well communicated by voices and gestures. A novel method for exploring emotion perception, by contrasting errors, is proposed. Studies often fail to consider whether the status of the perceiver affects emotion recognition abilities. Experiments presented here revealed an impact of mood, sex, and age of participants. Dysphoric mood was associated with difficulty in interpreting disgust from vocal and gestural channels. To some extent, this supports the concept that neural regions are specialised for the perception of disgust. Older participants showed decreased emotion recognition accuracy but no specific pattern of recognition difficulty. Sex of participant and of actor affected emotion recognition from voices. In order to examine neural mechanisms underlying emotion recognition, an exploration was undertaken using emotion tasks with Parkinson's patients. Patients showed no clear pattern of recognition impairment across channels of communication. In this study, the exclusion of surprise as a stimulus and response option in a facial emotion recognition task yielded results contrary to those achieved without this modification. Implications for this are discussed. Finally, this thesis gives rise to three caveats for neuropsychological research. First, the impact of the observers' status, in terms of mood, age, and sex, should not be neglected. Second, exploring multiple channels of communication is important for understanding emotion perception. Third, task design should be appraised before conclusions regarding impairments in emotion perception are presumed.

The aim of this thesis was to expand the existing knowledge base concerning the profile of emotional processing that is associated with major depression, particularly in terms of socially important non-verbal stimuli (e.g. emotional facial expressions). Experiment one utilised a face-word variant of the emotional Stroop task and demonstrated that depressed patients (DP) did not exhibit a selective attention bias for sad faces. Conversely, the healthy controls (HC) were shown to selectively attend to happy faces. At recognition memory testing, DP did not exhibit a memory bias for depression-relevant words, but did demonstrate a tendency to falsely recognise depression-relevant words that had not been presented at encoding. Experiment two examined the pattern of autobiographical memory (ABM) retrieval exhibited by DP and HC in response to verbal (words) and non-verbal (images & faces) affective cues. DP were slower than HC to retrieve positive ABMs, but did not differ from HC in their retrieval times for negative ABMs. Overall, DP retrieved fewer specific ABMs than did the HC. Participants retrieved more specific ABMs to image cues than to words or faces, but this pattern was only demonstrated by the HC. Reduced retrieval of specific ABMs by DP was a consequence of increased retrieval of categorical ABMs; this tendency was particularly marked when the participants were cued with faces. During experiment three, DP and HC were presented with a series of faces and were asked to identify the gender of the person featured in each photograph. Overall, gender identification times were not affected by the emotion portrayed by the faces. Furthermore at subsequent recognition memory testing, DP did not exhibit MCM bias for sad faces. During experiment four, DP and HC were presented with videotaped depictions of 'realistic' social interactions and were asked to identify the emotion portrayed by the characters and to make inferences about the thoughts, intentions and beliefs of these individuals. Overall, DP were impaired in their recognition of happiness and in understanding social interactions involving sarcasm and deception. Correct social inference was significantly related to both executive function and depression severity. Experiment five involved assessing a group of eight patients that had undergone neurosurgery for chronic, treatment-refractory depression on the identical emotion recognition and social perception tasks that were utilised in experiment four. Relative to HC, surgery patients (SP) exhibited general deficits on all emotion recognition and social processing tasks. Notably, depression status did not appear to interact with surgery status to worsen these observed deficits. These findings suggest that the anterior cingulate region of the prefrontal cortex may play a role in correct social inference. Summary: Taken together the findings of the five experimental studies of the thesis demonstrate that, in general, biases that have been observed in DP processing of affective verbal material generalise to non-verbal emotional material (e.g. emotional faces). However, there are a number of marked differences that have been highlighted throughout the thesis. There is also evidence that biased emotional processing in DP requires explicit processing of the emotional content of the stimuli. Furthermore, a central theme of the thesis is that deficits in executive function in DP appear to be implicated in the impairments of emotional processing that are exhibited by these patients.

O sinal de entrada na retina é decomposto em termos de frequência espacial (FE), variações periódicas de luminância ao longo do espaço. Existe vasta literatura sobre o processamento de FE no córtex visual primário. No entanto, não se sabe ao certo como esta informação sensorial básica é processada e integrada numa visão de alto nível. Esta tese aborda este tema ao investigar lateralidade cerebral, tempo de processamento e contexto cognitivo em três diferentes seções com objetivos específicos. Estas seções investigaram comportamentalmente visão de alto nível tendo a face humana como estímulo, dado sua relevância biológica e social. Na primeira seção (Theoretical Review), uma revisão apresenta estudos clínicos e neuropsicológicos que mostram áreas cerebrais envolvidas na percepção de faces e como os hemisférios esquerdo e direito realizam um processamento holístico e analítico baseado em informações de FEs. A especialização hemisférica de FE no reconhecimento de faces é então revisada e discutida. Concluiu-se que assimetrias sensoriais podem ser a base para assimetrias cognitivas de alta ordem. Ademais, foi destacado a influência do tempo de processamento. Na segunda seção (Study 1), foi investigado por método psicofísico a lateralidade de baixas e altas FEs no reconhecimento de faces em diferentes tempos de exposição. Faces com filtragem de FE foram apresentadas em campo visual dividido em alta e baixa restrição temporal em duas tarefas: reconhecimento facial (Experimento 1) e reconhecimento do sexo facial (Experimento 2). No Experimento 1, informações faciais de baixas e altas FEs foram mais eficientemente processadas no hemisfério direito e esquerdo, respectivamente, sem efeito do tempo de exposição das faces. Os resultados do Experimento 2 mostraram uma assimetria do hemisfério direito para baixas FEs em baixa restrição temporal. Conclui-se que o processamento de altas e baixas FEs é lateralizado nos hemisférios cerebrais no reconhecimento de faces. No entanto, a contribuição de altas e baixas FEs é dependente da tarefa e do tempo de exposição. Na terceira seção (Study 2) foi investigado qual estratégia temporal, coarse-to-fine (de baixas para altas FEs) ou fine-to-coarse, cada hemisfério cerebral utiliza para integrar informação de FE de faces humanas numa tarefa de categorização facial homem-mulher. Sequências dinâmicas breves coarse-to-fine e fine-to-coarse de faces foram apresentadas no campo visual esquerdo, direito e central. Os resultados do tempo de resposta e do score de eficiência invertida mostraram uma prevalência geral de um processamento coarse-to-fine, independente do campo visual de apresentação. Ainda, os dados da taxa de erro ressaltam o processamento coarse-to-fine realizado pelo hemisfério direito. No geral, esta tese fornece insights sobre assimetria cerebral funcional, integração de alto nível e curso temporal do processamento de FEs, principalmente para aqueles interessados na percepção de faces. Também foi mostrado que operações lateralizadas, tarefa-dependente e coarse-to-fine podem coexistir e interagir no cérebro para processar informação de FE. / Retinal input is decomposed in terms of spatial frequency (SF), i.e., periodic variations of luminance through space. There is extensive literature on the processing of SF in the primary visual cortex. However, it is still unclear how SF information is processed and integrated in high-level vision. This thesis addressed this issue in terms of laterality effects, processing time-course, and the cognitive context in three different sections with specific purposes. These sections behaviorally tackle high-level vision using human faces as stimuli due to their biological and social relevance. In the first section (Theoretical Review) a literature review presented clinical and neurophysiological studies that show brain areas that are involved in face perception and how the right and left hemispheres perform holistic and analytic processing, depending on SF information. The SF hemispheric specialization in face recognition is then reviewed and discussed. Our conclusion is that functional sensorial asymmetries may be the basis for high-level cognitive asymmetries. In addition, we highlighted the role of the processing time. In the second section (Study 1), we psychophysically investigated laterality of low and high SF in face recognition at different exposure times. The SF filtered faces were presented in a divided visual field at high and low temporal constraint in two tasks: face recognition (Experiment 1) and face gender recognition (Experiment 2). In Experiment 1, low and high SF facial information were more efficiently processed in the right and in the left hemisphere, respectively, with no effect of exposure time. In Experiment 2, results showed a right hemisphere asymmetry for low SF faces at low temporal constraint. We concluded that the processing of low and high SF is lateralized in the brain hemispheres for face recognition. However, low and high SF contribution is dependent on the task and the exposure time. In the third section (Study 2), we aimed to investigate which temporal strategy, i.e., coarse-to-fine (from low to high SF) or fine-to-course, each brain hemisphere performs to integrate SF information of human faces in a male-female categorization task. Coarse-to-fine and fine-to-course brief dynamic sequences of faces were presented in the left, right and central visual field. Results of the correct response time and the inverse efficiency score showed an overall advantage of coarse-to-fine processing, irrespective of the visual field of presentation. Data of the error rate also highlights the role of the right hemisphere in the coarse-to-fine processing. All in all, this thesis provided some insights on functional brain asymmetry, high-level integration, and processing time-course of SF information, mainly for those interested in face perception. It was also shown that lateralized, diagnostic-oriented, and coarse-to-fine operations may coexist and interact in the human brain to process SF information.

Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento. / Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.

Entre os sistemas de reconhecimento biométrico, a biometria da face exerce um papel importante nas atividades de pesquisa e nas aplicações de segurança, pois a face pode ser obtida sem conhecimento prévio de um indivíduo. Atualmente, uma grande quantidade de imagens digitais e seqüências de vídeo têm sido adquiridas principalmente sob condições não-controladas, freqüentemente com ruído, borramento, oclusão e variação de escala e iluminação. Por esses problemas, o reconhecimento facial (RF) é ainda considerado como uma área de pesquisa ativa e uma tarefa desafiadora. A motivação vem do fato que o reconhecimento de faces nas imagens com fundo complexo e em base de imagens faciais tem sido uma aplicação de sucesso. Portanto, o principal foco deste trabalho é reconhecer uma ou mais faces em imagens estáticas contendo diversos indivíduos e um individuo (face) em uma base de imagens com faces únicas obtidas sob condições diferentes. Para trabalhar com faces múltiplas, uma abordagem semi-supervisionada foi proposta baseada em características locais invariantes e discriminativas. A extração de características (EC) locais é feita utilizando-se do algoritmo Speeded-Up Robust Features (SURF). A busca por regiões nas quais as características ótimas podem ser extraídas é atendida através do algoritmo ABC. Os resultados obtidos mostram que esta abordagem é robusta e eficiente para aplicações de RF exceto para faces com iluminação não-uniforme. Muitos trabalhos de RF são baseados somente na extração de uma característica e nas abordagens de aprendizagem de máquina. Além disso, as abordagens existentes de EC usam características globais e/ou locais. Para obter características relevantes e complementares, a metodologia de RF deve considerar também as características de diferentes tipos e semi-globais. Portanto, a abordagem hierárquica de RF é proposta baseada na EC como globais, semi-globais e locais. As globais e semi-globais são extraídas utilizando-se de Color Angles (CA) e Edge Histogram Descriptors (EHD) enquanto somente características locais são extraídas utilizando-se do SURF. Uma ampla análise experimental foi feita utilizando os três métodos individualmente, seguido por um esquema hierárquico de três - estágios usando imagens faciais obtidas sob duas condições diferentes de iluminação com expressão facial e uma variação de escala leve. Além disso, para CA e EHD, o desempenho da abordagem foi também analisado combinando-se características globais, semi-globais e locais. A abordagem proposta alcança uma taxa de reconhecimento alta com as imagens de todas as condições testadas neste trabalho. Os resultados enfatizam a influência das características locais e semi-globais no desempenho do reconhecimento. Em ambas as abordagens, tanto nas faces únicas quanto nas faces múltiplas, a conquista principal é o alto desempenho obtido somente com a capacidade discriminativa de características sem nenhum esquema de treinamento. / Among biometric recognition systems, face biometrics plays an important role in research activities and security applications since face images can be acquired without any knowledge of individuals. Nowadays a huge amount of digital images and video sequences have been acquired mainly from uncontrolled conditions, frequently including noise, blur, occlusion and variation on scale and illumination. Because of these issues, face recognition (FR) is still an active research area and becomes a complex problem and a challenging task. In this context, the motivation comes from the fact that recognition of faces in digital images with complex background and databases of face images have become one of the successful applications of Computer Vision. Hence, the main goal of this work is to recognize one or more faces from still images with multiple faces and from a database of single faces obtained under different conditions. To work with multiple face images under varying conditions, a semi-supervised approach proposed based on the invariant and discriminative power of local features. The extraction of local features is done using Speeded-Up Robust Features (SURF). The search for regions from which optimal features can be extracted is fulfilled by an improved ABC algorithm. To fully exploit the proposed approach, an extensive experimental analysis was performed. Results show that this approach is robust and efficient for face recognition applications except for faces with non-uniform illumination. In the literature, a significant number of single FR researches are based on extraction of only one feature and machine learning approaches. Besides, existing feature extraction approaches broadly use either global or local features. To obtain relevant and complementary features from face images, a face recognition methodology should consider heterogeneous features and semi-global features. Therefore, a novel hierarchical semi-supervised FR approach is proposed based on extraction of global, semi-global and local features. Global and semi-global features are extracted using Color Angles (CA) and edge histogram descriptors (EHD) meanwhile only local features are extracted using SURF. An extensive experimental analysis using the three feature extraction methods was done first individually followed by a three-stage hierarchical scheme using the face images obtained under two different lighting conditions with facial expression and slight scale variation. Furthermore, the performance of the approach was also analyzed using global, semi-global and local features combinations for CA and EHD. The proposed approach achieves high recognition rates considering all image conditions tested in this work. In addition to this, the results emphasize the influence of local and semi-global features in the recognition performance. In both, single face and multiple faces approaches, the main achievement is the high performance obtained only from the discriminative capacity of extracted features without any training schemes.

This study examined the relationship between time of exposure to the human face and accurate subsequent photo line-up identification. A volunteer group of 124 undergraduate students was divided into three approximately equal sized subgroups. The three groups were then exposed to a video or a portion of a video depicting a theft. Exposure times ranged from two minutes to 30 seconds. The subjects were then given a questionnaire and shown a photo line-up of the mock perpetrator and five foils. Subjects were asked to identify the perpetrator and mark that identification on the questionnaire. Results of the experiment indicated that the longer a subject was exposed the greater the possibility of an accurate identification.