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Given the ecological importance of face recognition, it is not surprising that the visual system is capable of processing faces with remarkable efficiency. When presented with a face, information is rapidly extracted to detect and categorize it as a face, followed by face-specific information such as age, gender, and identity. According to cognitive and neural models, the processes underlying face recognition encompass a sequence of steps that begin with a perceptual or visual analysis followed by more image-invariant and identity-selective representations. Importantly, it is only familiar faces for which we have acquired long-term face memories that reach the final stages of identity processing to permit robust, image-invariant behavioural recognition. A key aspect of face processing is that it is fast and automatic. This can be said for both high-level categorization (i.e., detecting that a stimulus is a face) and for encoding at the identity-level. The purpose of these experiments was to use novel electrophysiological and psychophysical techniques to characterize these fast and automatic categorization processes. Experiment 1 and 2 used an implicit visual discrimination paradigm (fast periodic visual stimulation; FPVS) combined with electroencephalography (EEG) to isolate identity-specific neural responses to a personally familiar face, the own-face, and an unfamiliar stranger face. Experiment 1 showed that identity-specific responses recorded over the occipito-temporal region were stronger for a personally familiar face compared to the unfamiliar control identity, while the response to the own-face was even greater than to a personally familiar friend. In Experiment 2, identity-specific responses for a given identity were measured in participants both before and after real-world familiarization. As expected, the results showed a significant increase in the identity-specific response once participants became personally familiar with the test identities. In Experiment 3, we used saccadic eye movements to estimate the lower bounds of the speed of face categorization, and in particular to investigate the question of whether this categorization occurs during early feedforward processing. The results support the view that information needed to detect and selectively respond to face stimuli happens during the earliest visual processing. Collectively, these studies provide additional insight on the mechanisms underlying rapid and automatic face detection and face identity recognition. / Graduate

Main object of this work is to design and implement the algorithm for look direction determination, respectively the head movement. More specifically, it is a system that searches face in the video and then detects points, suitable for view direction estimation of tracked person. Estimation is realized using searching transformation, which has been performed on key points during head movement. For accuracy enhancement the calibration frames are used. Calibration frames determines the key points transformation in defined view directions. Main result is an application able to determine deflection of head from straight position in horizontal and vertical direction for tracked person. Output doesn't contain only information about deflection direction, but it also contains the size of deflection.

Este trabalho tem como objetivo desenvolver uma metodologia baseada em lógica nebulosa, (KLIR ; YUAN, 1995) para detectar faces humanas em imagens digitais. Considerando que pessoas conseguem reconhecer facilmente as faces humanas, este trabalho prevê a pesquisa da informação relativa a esse reconhecimento utilizando os resultados obtidos, em um esquema \"fuzzy\", para identificação de faces humanas em imagens digitais. É proposto então um algoritmo que classifique automaticamente as regiões de uma imagem em faces humanas ou não. O conhecimento para a construção da base de regras foi obtido através de informações das pessoas por meio de uma pesquisa de campo onde as respostas foram numericamente armazenadas para a geração da classificação nebulosa. Foram gerados desenhos line-draw que de uma maneira global representam as faces humanas. Esses desenhos foram apresentados às pessoas entrevistadas que forneceram subsídios para a montagem das regras \"fuzzy\". O algoritmo foi capaz de a partir daí, identificar faces humanas em imagens digitalizadas. Imagens simples contendo uma face frontal foram submetidas a um algoritmo e ao passarem por processamento (extração de bordas, erosão, binarização, etc...) perderam características, tornando difícil sua identificação. O algoritmo \"fuzzy\" foi capaz de atribuir um grau de pertinência à imagem dentro do conjunto de faces humanas frontais. A lógica nebulosa possui história recente, porém, desde cedo, demonstra sua versatilidade, principalmente por traduzir modelos não lineares ou imprecisos, os quais não apresentam convergência através de modelagem matemática convencional. / The present master dissertation aims to develop a methodology based on fuzzy pattern (KLIR; YUAN, 1995) to detect human faces in digital images. Considering that people are easily able to recognize human faces, this study foresees the research of the relative information to this recognition using the acquire results, in a \"fuzzy\" scheme, for the identification of human faces in digital images. It\'s proposed an algorithm which automatically classifies or not the regions of an image in human faces. It is based on the information acquired from people by means of a field research where the answers are stored numerically for the creation of the fuzzy classification. Drawings line-draw were created to represent human faces and were presented to the people interviewed to furnish information for the creation of the fuzzy rules. After that the algorithm was able to identify human faces in digitalized images. The algorithm utilizes simple images containing a frontal face, which lose their characteristics when they are processed (edges extration, erosion, binary image, etc...) and make their identification difficult. The fuzzy algorithm is also able to classify the images within the set of frontal human faces. The fuzzy logic has a recent history, however, it has always demonstrated its versatility, mainly regarding the translation of non-linear or inexact models which do not present conventional mathematical convergence through modeling.

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

Faces, along with the personal identities behind them, are effective elements in organizing a collection of consumer photos, as they represent who was involved. However, the accurate discrimination and subsequent recognition of face appearances is still very challenging. This can be attributed to the fact that faces are usually neither perfectly lit nor captured, particularly in the uncontrolled environments of consumer photos. Unlike, for instance, passport photos that only show faces stripped of their surroundings, Consumer Photo Collections contain a vast amount of meaningful context. For example, consecutively shot photos often correlate in time, location or scene. Further information can also be provided by the people appearing in photos, such as their demographics (ages and gender are often easier to surmise than identities), clothing, or the social relationships among co-occurring people. Motivated by this ubiquitous context, we propose and research people recognition approaches that consider contextual information within photos, as well as across entire photo collections. Our aim of leveraging additional contextual information (as opposed to only considering faces) is to improve recognition performance. However, instead of requiring users to explicitly label specific pieces of contextual information, we wish to implicitly learn and draw from the seemingly coherent content that exists inherently across an entire photo collection. Moreover, unlike conventional approaches that usually predict the identity of only one person’s appearance at a time, we lay out a semi-supervised approach to jointly recognize multiple peoples’ appearances across an entire photo collection simultaneously. As such, our aim is to find the overall best recognition solution. To make context-based joint recognition of people feasible, we research a sparse but efficient graph-based approach that builds on Markov Networks and utilizes distance-based face description methods. We show how to exploit the following specific contextual cues: time, social semantics, body appearances (clothing), gender, scene and ambiguous captions. We also show how to leverage crowd-sourced gamified feedback to iteratively improve recognition performance. Experiments on several datasets demonstrate and validate the effectiveness of our semisupervised graph-based recognition approach compared to conventional approaches.

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

The recognition accuracy of emotion in faces varies depending on the discrete emotion being expressed and the location of the stimulus. More specifically, emotion detection performance declines as facial stimuli are presented further out in the periphery. Interestingly, this is not always true for faces depicting happy emotional expressions, which can be associated with maintained levels of detection. The current study examined neurophysiological responses to emotional face discrimination in the periphery. Two event-related potentials (ERPs) that can be sensitive to the perception of emotion in faces, P1 and N170, were examined using EEG data recorded from electrodes at occipitotemporal sites on the scalp. Participants saw a face presented at a 0° angle of eccentricity, at a 10° angle of eccentricity, or at a 20° angle of eccentricity, and responded whether the face was a specific emotion or neutral. Results showed that emotion detection was higher when faces were presented at the center of the display than at 10° or 20° for both happy and angry expressions. Likewise, the voltage amplitude of the N170 component was greater when faces were presented at the center of the display than at 10° or 20°. Further exploration of the data revealed that high intensity expressions were more easily detected at each location and elicited a larger amplitude N170 than low intensity expressions for both emotions. For a peripheral emotion discrimination task like that which was employed in the current study, emotion cues seem to enhance face processing at peripheral locations.

<p>The purpose of this master’s thesis was to study the possibility to use computer vision methods to detect and classify objects in the front passenger seat in a car. This work presents different approaches to solve this problem and evaluates the usefulness of each technique. The classification information should later be used to modulate the speed and the force of the airbag, to be able to provide each occupant with optimal protection and safety.</p><p>This work shows that computer vision has a great potential in order to provide data, which may be used to perform reliable occupant classification. Future choice of method to use depends on many factors, for example costs and requirements on the system from laws and car manufacturers. Further, evaluation and tests of the methods in this thesis, other methods, the ABE approach and post-processing of the results should also be made before a reliable classification algorithm may be written.</p>

<p>Gaze tracking means to detect and follow the direction in which a person looks. This can be used in for instance human-computer interaction. Most existing systems illuminate the eye with IR-light, possibly damaging the eye. The motivation of this thesis is to develop a truly non-intrusive gaze tracking system, using only a digital camera, e.g. a web camera.</p><p>The approach is to detect and track different facial features, using varying image analysis techniques. These features will serve as inputs to a neural net, which will be trained with a set of predetermined gaze tracking series. The output is coordinates on the screen.</p><p>The evaluation is done with a measure of accuracy and the result is an average angular deviation of two to four degrees, depending on the quality of the image sequence. To get better and more robust results, a higher image quality from the digital camera is needed.</p>