This master‘s thesis describes the identification faces in real terms. It includes an overview of current methods of detection faces by the classifiers. It also includes various methods for detecting faces. The second part is a description of two programs designed to identify persons. The first program operates in real time under laboratory conditions, where using web camera acquires images of user's face. This program is designed to speed recognition of persons. The second program has been working on static images, in real terms. The main essence of this method is successful recognition of persons, therefore the emphasis on computational complexity. The programs I used a staged method of PCA, LDA and kernel PCA (KPCA). The first program only works with the PCA method, which has good results with respect to the success and speed of recognition. In the second program to compare methods, which passed the best method for KPCA.

Made available in DSpace on 2017-07-21T14:19:27Z (GMT). No. of bitstreams: 1 Voncarlos Marcelo Araujo.pdf: 3791024 bytes, checksum: c5d2b6c030643b2e46f5ae7004f73ca8 (MD5) Previous issue date: 2016-03-04 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The biodiversity of plant species plays a key role in the Earth's ecology, providing food, shelter and maintaining a healthy breathable atmosphere for all living beings. The plants also have medicinal properties and are used for alternative energy sources, such as biofuel. However, the number of plants endangered has gradually increased and the difficulties in the plants manual recognition process, does become a complex and slow task. A viable method for the identification of plants, or to provide a categorization of the plant, is the plant image acquisition and use pattern recognition techniques. In this way, the use of computers, despite having little contribution in the area, can provide important information on the taxonomy of plants, and can serve as a basis for systems that perform tasks such as the selection of certain plants or to guide the specialist for possible decision-making. This paper proposes a method for classification of plants based on collaborative images of the world experts. This method is able to deal with some complexities imposed during the capture of images, as the presence of noise (lighting, shadows and undesirable objects) and plants position variations. To accomplish this task are used texture descriptors based on SIFT, SURF and HOG, which have shown excellent results in several works. To enable testing of the proposed method, we used an image provided by the global task basis for recognition of plants in 2011, ImageCLEF, containing about 2,586 plant samples composed by 41 species divided into two distinct categories: the first one with 13 species and images with presence of noise, and with the second species and 28 sheets of images plotted on a white background. The results of the experiments show that the classifiers trained with texture descriptors are able to achieve good hit rates close to 70%, given the complexity of the problem. Classifiers combination methods have also been used and have been shown capable to improve the performance of classifiers, especially in the test with images that has the presence of noises. / A biodiversidade das espécies de plantas desempenha um papel fundamental na ecologia da Terra, fornecendo alimento, abrigo e mantendo uma atmosfera respirável saudável para todos os seres vivos. As plantas também têm propriedades medicinais e são utilizadas para fontes alternativas de energia, como o biocombustível. No entanto, o número de plantas em risco de extinção tem aumentado gradativamente e as dificuldades presentes no processo manual de reconhecimento de plantas, torna esta tarefa muito complexa e morosa. Uma metodologia viável para a identificação das plantas, ou para fornecer uma categorização de plantas, é a aquisição da imagem da planta e o uso técnicas de reconhecimento de padrões. Dessa forma, o uso da computação, apesar de ainda ter pequena contribuição na área, pode prover informações importantes sobre a taxonomia das plantas, além de poder servir como base para sistemas que executem tarefas como a de seleção de determinado tipo de plantas ou que guiem o especialista para possíveis tomadas de decisões. Neste trabalho é proposto um método para classificação de plantas baseado em imagens colaborativas de especialistas do mundo inteiro. Esse método é capaz de lidar com algumas complexidades impostas durante a captura das imagens, como a presença de ruídos (luminosidade, sombras e objetos indesejáveis) e variações de posições das plantas. Para cumprir essa tarefa são utilizados descritores de textura baseados em SIFT, SURF e HOG, que têm mostrado excelentes resultados em diversos trabalhos. Para possibilitar os testes do método proposto, foi empregada uma base de imagens disponibilizada pela tarefa mundial de reconhecimento de plantas em 2011, ImageCLEF, que contém cerca de 2.586 amostras de plantas composta por 41 espécies divididas em duas categorias distintas: a primeira com 13 espécies e imagens com presença de ruídos, e a segunda com 28 espécies e imagens de folhas plotadas em um fundo branco. Os resultados dos experimentos mostram que os classificadores treinados com descritores de textura são capazes de atingir boas taxas de acertos, próximas a 70%, dada a complexidade do problema. Métodos de combinação de classificadores também foram utilizados e se mostraram capazes de melhorar o desempenho dos classificadores, principalmente nos testes com imagens que tem a presença de ruídos.

Le but de cette thèse est de proposer des méthodes d'étude et des modèles pour l'analyse des signaux sociaux dans un contexte d'interaction en exploitant à la fois des techniques issues du traitement du signal et de la reconnaissance des formes.Tout d'abord, une méthode non supervisée permettant de mesurer l'imitation entre deux partenaires en termes de délai et de degré est proposée en étudiant uniquement des données gestuelles. Dans un premier temps, des points d'intérêts spatio-temporels sont détectés afin de sélectionner les régions les plus importantes des vidéos. Ils sont ensuite décrits à l'aide d'histogrammes pour permettre la construction de modèles sac-de-mots dans lesquels l'information spatiale est réintroduite. Le degré d'imitation et le délai entre les partenaires sont alors estimés de manière continue grâce à une corrélation-croisée entre les deux modèles sac-de-mots.La deuxième partie de cette thèse porte sur l'extraction automatique d'indices permettant de caractériser des interactions de groupe. Après avoir regroupé tous les indices couramment employés dans la littérature, nous avons proposé l'utilisation d'une factorisation en matrice non négative. En plus d'extraire les indices les plus pertinents, celle-ci a permis de regrouper automatiquement et de manière non supervisée des meetings en 3 classes correspondant aux trois types de leadership tels que définis par les psychologues.Enfin, la dernière partie se focalise sur l'extraction non supervisée d'indices permettant de caractériser des groupes. La pertinence de ces indices, par rapport à des indices ad-hoc provenant de l'état de l'art, est ensuite validée dans une tâche de reconnaissance des rôles. / In a social interaction, we adapt our behavior to our interlocutors. Studying and understanding the underlying mecanisms of this adaptation is the center of Social Signal Processing. The goal of this thesis is to propose methods of study and models for the analysis of social signals in the context of interaction, by exploiting both social processing and pattern recognition techniques. First, an unsupervised method allowing the measurement of imitation between two partners in terms of delay and degree is proposed, only using gestual data. Spatio-temporal interest point are first detected in order to select the most important regions of videos. Then they are described by histograms in order to construct bag-of-words models in which spatial information is reintroduced. Imitation degree and delay between partners are estimated in a continuous way thanks to cross-correlation between the two bag-of-words models. The second part of this thesis focus on the automatic extraction of features permitting to characterizing group interactions. After regrouping all features commonly used in literature, we proposed the utilization of non-negative factorization. More than only extracting the most pertinent features, it also allowed to automatically regroup, and in an unsupervised manner, meetings in three classes corresponding to three types of leadership defined by psychologists. Finally, the last part focus on unsupervised extraction of features permitting to characterize groups. The relevance of these features, compared to ad-hoc features from state of the art, is then validated in a role recognition task.

The increase of mobile smartphones continues to grow and with it the demand for automation and use of the most offered aspects of the phone, whether in medicine (health care and surveillance) or in user applications (automatic recognition of position, etc.). As part of this work has been created the designs and implementation of the system for the recognition of human activity on the basis of data processing from sensors of smartphones, along with the determination of the optimal parameters, recovery success rate and comparison of individual evaluation. Other benefits include a draft format and displaying numerous training set consisting of real contributions and their manual evaluation. In addition to the main benefits, the software tool was created to allow the validation of the elements of the training set and acquisition of features from this set and software, that is able with the help of deep learning to train models and then test them.

Dans cette thèse, nous abordons le problème de l'analyse des sentiments. Plus précisément, nous sommes intéressés à analyser le sentiment exprimé dans les textes de médias sociaux.Nous allons nous concentrer sur deux tâches principales: la détection de polarité de sentiment dans laquelle nous cherchons à déterminer la polarité (positive, négative ou neutre) d'un texte donné et l'extraction de cibles d’opinion et le sentiment exprimé vers ces cibles (par exemple, pour le restaurant nous allons extraire des cibles comme la nourriture, pizza, service). Notre principal objectif est de construire des systèmes à la pointe de la technologie qui pourrait faire les deux tâches. Par conséquent, nous avons proposé des systèmes supervisés différents suivants trois axes de recherche: l'amélioration de la performance du système par la pondération de termes, en enrichissant de la représentation de documents et en proposant un nouveau modèle pour la classification de sentiment.Pour l'évaluation, nous avons participé à un atelier international sur l'évaluation sémantique (Sem Eval), nous avons choisi deux tâches: l'analyse du sentiment sur Twitter dans laquelle nous déterminer la polarité d'un tweet et l'analyse des sentiments basée sur l’aspect dans laquelle nous extrayons les cibles d'opinion dans les critiques de restaurants, puis nous déterminons la polarité de chaque cible, nos systèmes ont été classés parmi les premiers trois meilleurs systèmes dans toutes les sous-tâches. Nous avons également appliqué nos systèmes sur un corpus des critiques de livres français construit par l'équipe Open Edition pour extraire les cibles d'opinion et leurs polarités. / In this thesis, we address the problem of sentiment analysis. More specifically, we are interested in analyzing the sentiment expressed in social media texts such as tweets or customer reviews about restaurant, laptop, hotel or the scholarly book reviews written by experts. We focus on two main tasks: sentiment polarity detection in which we aim to determine the polarity (positive, negative or neutral) of a given text and the opinion target extraction in which we aim to extract the targets that the people tend to express their opinions towards them (e.g. for restaurant we may extract targets as food, pizza, service).Our main objective is constructing state-of-the-art systems which could do the two tasks. Therefore, we have proposed different supervised systems following three research directions: improving the system performance by term weighting, by enriching the document representation and by proposing a new model for sentiment classification. For evaluation purpose, we have participated at an International Workshop on Semantic Evaluation (SemEval), we have chosen two tasks: Sentiment analysis in twitter in which we determine the polarity of a tweet and Aspect-Based sentiment analysis in which we extract the opinion targets in restaurant reviews, then we determine the polarity of each target. Our systems have been among the first three best systems in all subtasks. We also applied our systems on a French book reviews corpus constructed by OpenEdition team for extracting the opinion targets and their polarities.

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