<p>The aim of the present thesis was to study sex differences in memory and other cognitive bilities in healthy adults. In Study I, participants performed a number of episodic memory tasks that were more or less verbal in nature. Results showed that women performed on a higher level than did men in the episodic memory tasks where it was possible to use verbal labels, whereas men performed on a higher level than did women in a visuospatial episodic memory task. In Study II, women’s advantage in face recognition was investigated.Results showed that women performed at a higher level than did men only in the recognition of other women’s faces. In Study III, sex differences in cognitive tasks as well as brain measures were investigated in healthy older adults. Results showed that only the sex differences in a motor task could, to some extent, be explained by sex differences in one of the brain measures. The findings, as well as possible explanations for these patterns of results, are discussed in a theoretical context.</p>

O reconhecimento de expressões faciais de emoções (REFE) é essencial para a interação social cotidiana e a comunicação interpessoal e é considerado um aspecto importante da cognição social. É uma condição inata e universal nos seres humanos, mas pode ser influenciada por diferentes variáveis sociodemográficas como sexo, idade e escolaridade. Apesar do número expressivo de estudos relacionados ao REFE, não existe uma tarefa padrão utilizada em sua avaliação e as que se encontram disponíveis nem sempre passaram por um procedimento de padronização e normatização. O presente estudo propôs-se a avaliar comparativamente três diferentes tarefas de REFE utilizadas no contexto brasileiro, e disponibilizar um procedimento normatizado às características socioculturais de nossa população, considerando as variáveis anteriormente destacadas, em uma amostra da população geral, estimada estatisticamente (n=240). Para composição desta amostra, foram incluídos sujeitos de ambos os sexos, com idade entre 18 e 75 anos, sem prejuízos intelectuais e com variados níveis de escolaridade. A coleta de dados foi realizada individualmente. Os dados referentes a acurácia, tempo de resposta e intensidade para o reconhecimento das expressões foram salvos automaticamente pelo programa computacional gerador da atividade e alocados manualmente em um banco de dados. Utilizou-se para as análises estatísticas o programa Statistical Package for the Social Sciences (SPSS). Os dados sociodemográficos da amostra e aqueles relacionados ao desempenho nas tarefas foram analisados através de testes estatísticos descritivos e paramétricos (teste t de Student, Anova, Qui-Quadrado e Teste de Correlação de Pearson). O tamanho de efeito das diferenças foi calculado por meio do eta2 parcial e do d de Cohen. Para realizar o procedimento de normatização calculou-se a distribuição dos dados em percentis de acordo com cada tarefa e grupo estudado. Os resultados apontaram para uma ligeira superioridade das mulheres no REFE em tarefas dinâmicas, maior prejuízo do grupo na faixa etária dos 61-75 anos independente da tarefa, e menor acurácia dos sujeitos com menor escolaridade na tarefa estática. No que diz respeito às tarefas utilizadas observou-se que a acurácia foi maior na tarefa dinâmica preto e branco e que a tarefa estática exigiu maior tempo de resposta mas menor intensidade de emoção para o REFE. Em análises comparativas a tarefa dinâmica pareceu ser aquela com maior proximidade à condição real de contato humano. Considerando que a TDPB foi aquela com maior média de acerto considerou-se esta a mais apropriada para uso no nosso contexto. O estudo apresentou/disponibilizou ainda dados normativos para o contexto brasileiro em função do sexo, idade e escolaridade, que poderão servir como parâmetro de comparação em outros estudos clínicos, bem como para pesquisa de avaliação do reconhecimento emocional. / The recognition of facial expressions of emotions (REFE) is essential for everyday social interaction and interpersonal communication and is considered an important aspect of social cognition. It is an innate and universal condition in humans, but it can be influenced by different sociodemographic variables such as sex, age and schooling. Despite the significant number of studies related to the REFE, there is no standard task used in its evaluation and those that are available do not always undergo a procedure of standardization and normatization. The aim of this study was to compare three different REFE tasks used in the Brazilian context and to provide a normalized procedure to the sociocultural characteristics of our population, considering the variables previously mentioned in a statistically significant sample of the general population (n= 240 ). For the composition of this sample, subjects of both genders, aged between 18 and 75 years, without intellectual losses and with varying levels of schooling were included. Data collection was done individually. The data referring to the accuracy, response time and intensity for the recognition of the expressions were automatically saved by the computational program generating the activity and manually allocated in a database. Statistical Package for the Social Sciences (SPSS) was used for statistical analysis. The sociodemographic data of the sample and those related to performance in the tasks were analyzed through descriptive and parametric statistical tests (Student\'s t test, Anova, Chi-Square and Pearson\'s correlation test). The effect size of the differences was calculated by means of partial eta2 and Cohen\'s d. To perform the standardization procedure, the distribution of the data in percentiles was calculated according to each task and group studied. The results pointed to a slight superiority of the women in the REFE in dynamic tasks, greater loss of the group in the age group of 61-75 years independent of the task, and lower accuracy of the subjects with less schooling in the static task. Regarding the tasks used it was observed that the accuracy was higher in the black and white dynamic task and that the static task required a longer response time but a lower intensity of emotion for the REFE. In comparative analyzes the dynamic task seemed to be the one with the closest proximity to the real condition of human contact. Considering that the TDPB was the one with the highest average accuracy, it was considered that this was the most appropriate for use in our context. The study presented / provided normative data for the Brazilian context based on sex, age and schooling, which may serve as a benchmark in other clinical studies, as well as for the evaluation of emotional recognition.

This project is a collaboration with the company JayWay in Halmstad. In order to enter theoffice today, a tag-key is needed for the employees and a doorbell for the guests. If someonerings the doorbell, someone on the inside has to open the door manually which is consideredas a disturbance during work time. The purpose with the project is to minimize thedisturbances in the office. The goal with the project is to develop a system that uses facerecognition and speech-to-text to control the lock system for the entrance door. The components used for the project are two Raspberry Pi’s, a 7 inch LCD-touch display, aRaspberry Pi Camera Module V2, a external sound card, a microphone and speaker. Thewhole project was written in Python and the platform used was Amazon Web Services (AWS)for storage and the face recognition while speech-to-text was provided by Google.The system is divided in three functions for employees, guests and deliveries. The employeefunction has two authentication steps, the face recognition and a random generated code that needs to be confirmed to avoid biometric spoofing. The guest function includes the speech-to-text service to state an employee's name that the guest wants to meet and the employee is then notified. The delivery function informs the specific persons in the office that are responsiblefor the deliveries by sending a notification.The test proves that the system will always match with the right person when using the facerecognition. It also shows what the threshold for the face recognition can be set to, to makesure that only authorized people enters the office.Using the two steps authentication, the face recognition and the code makes the system secureand protects the system against spoofing. One downside is that it is an extra step that takestime. The speech-to-text is set to swedish and works quite well for swedish-speaking persons.However, for a multicultural company it can be hard to use the speech-to-text service. It canalso be hard for the service to listen and translate if there is a lot of background noise or ifseveral people speak at the same time.

This study aimed to understand the differences in strength or coordination of brain regions involved in processing faces in the presence of aging and/or progressing neuropathology (Alzheimer's disease). To this end, Experiment 1 evaluated age-related differences in basic face processing and the effects of familiarity in face processing. Overall, face processing in younger (22-35yrs) and older participants (63-83yrs) recruited a broadly distributed network of brain activity, but the distribution of activity varied depending on the age of the individual. The younger population utilized regions of the occipitotemporal, medial frontal and posterior parietal cortices while the older population recruited a concentrated occipitotemporal network. The younger participants were also sensitive to the type of face presented, as Novel faces were associated with greater mean BOLD activity than either the Famous or Relatives faces. Interestingly, Relatives faces were associated with greater mean B OLD activity in more regions of the brain than found in any other analysis in Exp. 1, spanning the inferior frontal, medial temporal and inferior parietal cortices. In contrast, the older adults were not sensitive to the type of face presented, which could reflect a difference in cognitive strategies used by the older population when presented with this type of face stimuli. Experiment 2 evaluated face processing, familiarity in face processing and also emphasized the interactive roles autobiographical processing and memory recency play in processing familiar faces in mature adults (MA; 45-55yrs), older adults (OA; 70-92yrs) and patients suffering from Alzheimer's disease (AD; 70-92yrs). / MA participants had greater mean BOLD activity values in more regions of the brain than observed in either of the older adult populations, spanning regions of the medial frontal, medial temporal, inferior parietal and occipital cortices. OA, in contrast, utilized a concentrated frontal and medial temporal network and AD participants had the greatest deficit in BOLD activity overall.Age-related differences in processing faces, in processing the type of face presented, in autobiographical information processing and in processing the recency of a memory were noted, as well as differences due to the deleterious effects of AD. / by Jeanna Winchester. / Thesis (Ph.D.)--Florida Atlantic University, 2009. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2009. Mode of access: World Wide Web.

Two experiments were conducted to investigate the impact of poser and perceiver gender on the Happiness/Anger Superiority effect and the Female Advantage in facial expression recognition. Happy, neutral, and angry facial expressions were presented on male and female faces under Continuous Flash Suppression (CFS). Participants of both genders indicated when the presented faces broke through the suppression. In the second experiment, angry and happy expressions were reduced to 50% intensity. At full intensity, there was no difference in the reaction time for female neutral and angry faces, but male faces showed a difference in detection between all expressions. Across experiments, male faces were detected later than female faces for all facial expressions. Happiness was generally detected faster than anger, except when on female faces at 50% intensity. No main effect for perceiver gender emerged. It was concluded that happiness is superior to anger in CFS, and that poser gender affects facial expression recognition. / by Sophia Peaco. / Thesis (M.A.)--Florida Atlantic University, 2013. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente. / Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.

O presente trabalho teve por objetivo pesquisar os efeitos que as bandas de freqüência espacial alta e baixa têm no reconhecimento de faces em campo visual lateralizado. Foram distribuídos aleatoriamente 40 participantes em dois grupos. Os voluntários observaram 14 fotos de faces sem filtragem até memorizá-las. A seguir foram apresentadas 56 fotos de faces com filtragens de freqüências espaciais, intercaladas aleatoriamente com apresentações de faces não mostradas anteriormente. Cada uma delas foi exibida na tela durante 300 ms mediante a metodologia de apresentação dicótica. O participante devia responder se a face mostrada pertencia ao grupo de fotos inicialmente observado. As freqüências de respostas permitiram calcular as curvas ROC (Receiver Operating Characteristic) e os parâmetros Az e da preconizado pela Teoria de Detecção de Sinal (MACMILLAN; CREELMAN, 2005) para as faces naturais, faces compostas de freqüências espaciais baixas e faces compostas de freqüências espaciais altas. Os resultados obtidos mostram que as faces Originais foram melhor reconhecidas do que as faces com Freqüências Espaciais Altas (FEA) ou Freqüências Espaciais Baixas (FEB). Ao contrário do achado na literatura, o Hemisfério Esquerdo (HE) teve uma tendência a reconhecer mais eficazmente as faces do que o Hemisfério Direito (HD), independente da condição de filtragem. O HD é igualmente competente do que o HE para processar FEB, mas pior do que o HE para processar FEA. Quanto à performance por gênero, tanto homens quanto mulheres tiveram um desempenho similar quando as faces foram processadas com o HD. O desempenho das mulheres ficou de acordo com a hipótese da FE, pois o reconhecimento que fizeram para as faces com FEA foi melhor do que para as que tinham predomínio de FEB. Os homens, mesmo com o HE, fizeram um reconhecimento melhor das faces com predomínio de FEB do que das faces com FEA. / This study was made with the objective of investigate the effects of high and low spatial frequency bands in face recognition in lateralized visual field. 40 participants were randomly distributed in two groups. The volunteers viewed fourteen non-filtered pictures of faces until they managed to memorize them. After that, fifty six spatial frequency filtered pictures of faces were presented randomly interspersed with pictures of faces previously showed. Each one of them was exhibited in the screen for three hundred milliseconds using the dichotic presentation procedure. The participant should answer whether the face presented belonged to the group of pictures initially viewed. The frequency of responses allowed to calculate the ROC (Receiver Operating Characteristic) Curves and the Az and da parameters praised by the Signal Detection Theory (Macmillan; Creelman, 2005) for natural faces, low spatial frequency composed faces and high spatial frequency composed faces. Results showed that original faces were better recognized than faces with high spatial frequencies (HSF) and low spatial frequencies (LSF). Differently from literature, the left hemisphere was more accurate than the right to recognize faces, regardless of the filter condition. The RH was equivalent to the LH to process LSF, but worse than the LH to process HSF. Concerning the performance of the genders, men and women judged faces in a very similar way when they used the RH. The performance of women agreed with the FE hypothesis, being faces with HSF recognized better than faces with LSF. Men, even using the LH, were more accurate to recognize faces with LSF than HSF.

Many errors in recognition are made because various features of a stimulus are attended inefficiently. Those features are not bound together and can then be confused with other information. One of the most common types of these errors is conjunction errors. These happen when mismatched features of memories are combined to form a composite memory. This study tests how likely conjunction errors, along with other recognition errors, occur when participants watch videos of people both with and without unusual facial features performing actions after a week time lag. It was hypothesized that participants would falsely recognize actresses in the conjunction item condition over the other conditions. The likelihood of falsely recognizing a new person increased when presented with a feature, but the conjunction items overall were most often falsely recognized. / by Autumn Keif. / Thesis (M.A.)--Florida Atlantic University, 2012. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2012. Mode of access: World Wide Web.

A biometria é a ciência que estuda a mensuração dos seres vivos. Muitos trabalhos exploram as características dos seres humanos tais como, impressão digital, íris e face, a fim de desenvolver sistemas biométricos, utilizados em diversas aplicações (monitoramento de segurança, computação ubíqua, robótica). O reconhecimento de faces é uma das técnicas biométricas mais investigadas, por ser bastante intuitiva e menos invasiva que as demais. Alguns trabalhos envolvendo essa técnica se preocupam apenas em localizar a face de um indivíduo (fazer a contagem de pessoas), enquanto outros tentam identificá-lo a partir de uma imagem. Este trabalho propõe uma abordagem capaz de identificar faces a partir de quadros de vídeo e, posteriormente, reconhecê-las por meio de técnicas de análise de imagens. Pode-se dividir o trabalho em dois módulos principais: (1) - Localização e rastreamento de faces em uma seqüência de imagens ( frames), além de separar a região rastreada da imagem; (2) - Reconhecimento de faces, identificando a qual pessoa pertence. Para a primeira etapa foi implementado um sistema de análise de movimento (baseado em subtração de quadros) que possibilitou localizar, rastrear e captar imagens da face de um indivíduo usando uma câmera de vídeo. Para a segunda etapa foram implementados os módulos de redução de informações (técnica Principal Component Analysis - PCA), de extração de características (transformada wavelet de Gabor), e o de classificação e identificação de face (distância Euclidiana e Support Vector Machine - SVM). Utilizando-se duas bases de dados de faces (FERET e uma própria - Própria), foram realizados testes para avaliar o sistema de reconhecimento implementado. Os resultados encontrados foram satisfatórios, atingindo 91,92% e 100,00% de taxa de acertos para as bases FERET e Própria, respectivamente. / Biometry is the science of measuring and analyzing biomedical data. Many works in this field have explored the characteristics of human beings, such as digital fingerprints, iris, and face to develop biometric systems, employed in various aplications (security monitoring, ubiquitous computation, robotic). Face identification and recognition are very apealing biometric techniques, as it it intuitive and less invasive than others. Many works in this field are only concerned with locating the face of an individual (for counting purposes), while others try to identify people from faces. The objective of this work is to develop a biometric system that could identify and recognize faces. The work can be divided into two major stages: (1) Locate and track in a sequence of images (frames), as well as separating the tracked region from the image; (2) Recognize a face as belonging to a certain individual. In the former, faces are captured from frames of a video camera by a motion analysis system (based on substraction of frames), capable of finding, tracking and croping faces from images of individuals. The later, consists of elements for data reductions (Principal Component Analysis - PCA), feature extraction (Gabor wavelets) and face classification (Euclidean distance and Support Vector Machine - SVM). Two faces databases have been used: FERET and a \"home-made\" one. Tests have been undertaken so as to assess the system\'s recognition capabilities. The experiments have shown that the technique exhibited a satisfactory performance, with success rates of 91.97% and 100% for the FERET and the \"home-made\" databases, respectively.

O reconhecimento facial é uma tarefa que os seres humanos realizam naturalmente todos os dias e praticamente sem esforço nenhum. Porém para uma máquina este processo não é tão simples. Com o aumento do poder computacional das máquinas atuais criou-se um grande interesse no processamento de imagens e vídeos digitais, com aplicações nas mais diversas áreas de conhecimento. Este trabalho objetiva a comparação de técnicas de reconhecimento facial, já conhecidas na literatura, com o intuito de identificar qual técnica possui melhor desempenho em um ambiente real e semicontrolado. Secundariamente avalia-se a possibilidade da utilização de uma ou mais técnicas de reconhecimento facial para identificar automaticamente a presença de alunos em uma sala de aula de artes marciais, utilizando imagens das câmeras de vigilância instaladas no recinto, levando em consideração aspectos importantes, tais como: imagens com pouca nitidez, luminosidade não ideal, movimentação constante dos alunos e o fato das câmeras estarem em um ângulo fixo. Este trabalho está relacionado às áreas de Processamento de Imagens e Reconhecimento de Padrões, e integra a linha de pesquisa de \"Monitoramento de Presença\" do projeto \"Ensino e Monitoramento de Atividades Físicas via Técnicas de Inteligência Artificial\" (Processo 2014.1.923.86.4, publicado no DOE 125(45), em 10/03/2015), projeto este executado em conjunto da Universidade de São Paulo, Faculdade Campo Limpo Paulista e Academia Central Kungfu-Wushu. Com os experimentos realizados e apresentados neste trabalho foi possível concluir que, dentre os métodos de reconhecimento facial utilizados, o método Local Binary Patterns teve o melhor desempenho no ambiente proposto. Por outro lado, o método Eigenfaces teve o pior desempenho de acordo com os experimentos realizados. Além disso, foi possível concluir também que não é viável a realização da detecção de presença automática de forma confiável no ambiente proposto, pois a taxa de reconhecimento facial foi relativamente baixa, se comparada a outros trabalhos do estado da arte, trabalhos estes que usam de ambientes de testes mais amigáveis, mas ao mesmo tempo menos comumente encontrados em nosso dia-a-dia. Acredita-se que foi possível alcançar os objetivos propostos pelo trabalho e que o mesmo possa contribuir para o estado da arte atual na área de visão computacional, mais precisamente no âmbito do reconhecimento facial. Ao final são sugeridos alguns trabalhos futuros que podem ser utilizados como ponto de partida para a continuação desta pesquisa ou até mesmo de novas pesquisas relacionadas a este tema / Face recognition is a task that human beings perform naturally in their everyday lives, usually with no effort at all. To machines, however, this process is not so simple. With the increasing computational power of current machines, a great interest was created in the field of digital videos and images processing, with applications in most diverse areas of knowledge. This work aims to compare face recognition techniques already know in the literature, in order to identify which technique has the best performance in a real and semicontrolled environment. As a secondary objective, we evaluate the possibility of using one or more face recognition techniques to automatically identify the presence of students in a martial arts classroom using images from the surveillance cameras installed in the room, taking into account important aspects such as images with low sharpness, illumination variation, constant movement of students and the fact that the cameras are at a fixed angle. This work is related to the Image Processing and Pattern Recognition areas, and integrates the research line \"Presence Monitoring\" of the project entitled \"Education and Monitoring of Physical Activities using Artificial Intelligence Techniques\" (Process 2014.1.923.86.4, published in DOE 125 (45) on 03/10/2015), developed as a partnership between the University of São Paulo, Campo Limpo Paulista Faculty, and Kungfu-Wushu Central Academy. With the experiments performed and presented in this work it was possible to conclude that, amongst all face recognition methods that were tested, Local Binary Patterns had the best performance in the proposed environment. On the other hand, Eigenfaces had the worse performance according to the experiments. Moreover, it was also possible to conclude that it is not feasible to perform the automatic presence detection reliably in the proposed environment, since the face recognition rate was relatively low, compared to the state of the art which uses, in general, more friendly test environments but at the same time less likely found in our daily lives. We believe that it was possible to achieve the objectives proposed by this work and that can contribute to the current state of the art in the computer vision field and, more precisely, in the face recognition area. Finally, some future work is suggested that can be used as a starting point for the continuation of this work or even for new researches related to this topic