Local Binary Patterns in Focal-Plane Processing. Analysis and Applications

Feature extraction is the part of pattern recognition, where the sensor data is transformed into a more suitable form for the machine to interpret. The purpose of this step is also to reduce the amount of information passed to the next stages of the system, and to preserve the essential information in the view of discriminating the data into different classes. For instance, in the case of image analysis the actual image intensities are vulnerable to various environmental effects, such as lighting changes and the feature extraction can be used as means for detecting features, which are invariant to certain types of illumination changes. Finally, classification tries to make decisions based on the previously transformed data. The main focus of this thesis is on developing new methods for the embedded feature extraction based on local non-parametric image descriptors. Also, feature analysis is carried out for the selected image features. Low-level Local Binary Pattern (LBP) based features are in a main role in the analysis. In the embedded domain, the pattern recognition system must usually meet strict performance constraints, such as high speed, compact size and low power consumption. The characteristics of the final system can be seen as a trade-off between these metrics, which is largely affected by the decisions made during the implementation phase. The implementation alternatives of the LBP based feature extraction are explored in the embedded domain in the context of focal-plane vision processors. In particular, the thesis demonstrates the LBP extraction with MIPA4k massively parallel focal-plane processor IC. Also higher level processing is incorporated to this framework, by means of a framework for implementing a single chip face recognition system. Furthermore, a new method for determining optical flow based on LBPs, designed in particular to the embedded domain is presented. Inspired by some of the principles observed through the feature analysis of the Local Binary Patterns, an extension to the well known non-parametric rank transform is proposed, and its performance is evaluated in face recognition experiments with a standard dataset. Finally, an a priori model where the LBPs are seen as combinations of n-tuples is also presentedSiirretty Doriast

Reconhecimento de padrões em expressões faciais : algoritmos e aplicações

Orientador: Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de emoções tem-se tornado um tópico relevante de pesquisa pela comunidade científica, uma vez que desempenha um papel essencial na melhoria contínua dos sistemas de interação humano-computador. Ele pode ser aplicado em diversas áreas, tais como medicina, entretenimento, vigilância, biometria, educação, redes sociais e computação afetiva. Há alguns desafios em aberto relacionados ao desenvolvimento de sistemas emocionais baseados em expressões faciais, como dados que refletem emoções mais espontâneas e cenários reais. Nesta tese de doutorado, apresentamos diferentes metodologias para o desenvolvimento de sistemas de reconhecimento de emoções baseado em expressões faciais, bem como sua aplicabilidade na resolução de outros problemas semelhantes. A primeira metodologia é apresentada para o reconhecimento de emoções em expressões faciais ocluídas baseada no Histograma da Transformada Census (CENTRIST). Expressões faciais ocluídas são reconstruídas usando a Análise Robusta de Componentes Principais (RPCA). A extração de características das expressões faciais é realizada pelo CENTRIST, bem como pelos Padrões Binários Locais (LBP), pela Codificação Local do Gradiente (LGC) e por uma extensão do LGC. O espaço de características gerado é reduzido aplicando-se a Análise de Componentes Principais (PCA) e a Análise Discriminante Linear (LDA). Os algoritmos K-Vizinhos mais Próximos (KNN) e Máquinas de Vetores de Suporte (SVM) são usados para classificação. O método alcançou taxas de acerto competitivas para expressões faciais ocluídas e não ocluídas. A segunda é proposta para o reconhecimento dinâmico de expressões faciais baseado em Ritmos Visuais (VR) e Imagens da História do Movimento (MHI), de modo que uma fusão de ambos descritores codifique informações de aparência, forma e movimento dos vídeos. Para extração das características, o Descritor Local de Weber (WLD), o CENTRIST, o Histograma de Gradientes Orientados (HOG) e a Matriz de Coocorrência em Nível de Cinza (GLCM) são empregados. A abordagem apresenta uma nova proposta para o reconhecimento dinâmico de expressões faciais e uma análise da relevância das partes faciais. A terceira é um método eficaz apresentado para o reconhecimento de emoções audiovisuais com base na fala e nas expressões faciais. A metodologia envolve uma rede neural híbrida para extrair características visuais e de áudio dos vídeos. Para extração de áudio, uma Rede Neural Convolucional (CNN) baseada no log-espectrograma de Mel é usada, enquanto uma CNN construída sobre a Transformada de Census é empregada para a extração das características visuais. Os atributos audiovisuais são reduzidos por PCA e LDA, então classificados por KNN, SVM, Regressão Logística (LR) e Gaussian Naïve Bayes (GNB). A abordagem obteve taxas de reconhecimento competitivas, especialmente em dados espontâneos. A penúltima investiga o problema de detectar a síndrome de Down a partir de fotografias. Um descritor geométrico é proposto para extrair características faciais. Experimentos realizados em uma base de dados pública mostram a eficácia da metodologia desenvolvida. A última metodologia trata do reconhecimento de síndromes genéticas em fotografias. O método visa extrair atributos faciais usando características de uma rede neural profunda e medidas antropométricas. Experimentos são realizados em uma base de dados pública, alcançando taxas de reconhecimento competitivasAbstract: Emotion recognition has become a relevant research topic by the scientific community, since it plays an essential role in the continuous improvement of human-computer interaction systems. It can be applied in various areas, for instance, medicine, entertainment, surveillance, biometrics, education, social networks, and affective computing. There are some open challenges related to the development of emotion systems based on facial expressions, such as data that reflect more spontaneous emotions and real scenarios. In this doctoral dissertation, we propose different methodologies to the development of emotion recognition systems based on facial expressions, as well as their applicability in the development of other similar problems. The first is an emotion recognition methodology for occluded facial expressions based on the Census Transform Histogram (CENTRIST). Occluded facial expressions are reconstructed using an algorithm based on Robust Principal Component Analysis (RPCA). Extraction of facial expression features is then performed by CENTRIST, as well as Local Binary Patterns (LBP), Local Gradient Coding (LGC), and an LGC extension. The generated feature space is reduced by applying Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for classification. This method reached competitive accuracy rates for occluded and non-occluded facial expressions. The second proposes a dynamic facial expression recognition based on Visual Rhythms (VR) and Motion History Images (MHI), such that a fusion of both encodes appearance, shape, and motion information of the video sequences. For feature extraction, Weber Local Descriptor (WLD), CENTRIST, Histogram of Oriented Gradients (HOG), and Gray-Level Co-occurrence Matrix (GLCM) are employed. This approach shows a new direction for performing dynamic facial expression recognition, and an analysis of the relevance of facial parts. The third is an effective method for audio-visual emotion recognition based on speech and facial expressions. The methodology involves a hybrid neural network to extract audio and visual features from videos. For audio extraction, a Convolutional Neural Network (CNN) based on log Mel-spectrogram is used, whereas a CNN built on Census Transform is employed for visual extraction. The audio and visual features are reduced by PCA and LDA, and classified through KNN, SVM, Logistic Regression (LR), and Gaussian Naïve Bayes (GNB). This approach achieves competitive recognition rates, especially in a spontaneous data set. The second last investigates the problem of detecting Down syndrome from photographs. A geometric descriptor is proposed to extract facial features. Experiments performed on a public data set show the effectiveness of the developed methodology. The last methodology is about recognizing genetic disorders in photos. This method focuses on extracting facial features using deep features and anthropometric measurements. Experiments are conducted on a public data set, achieving competitive recognition ratesDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação140532/2019-6CNPQCAPE

Discriminative Appearance Models for Face Alignment

The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent

Dynamic gesture recognition using transformation invariant hand shape recognition

In this thesis a detailed framework is presented for accurate real time gesture recognition. Our approach to develop a hand-shape classifier, trained using computer animation, along with its application in dynamic gesture recognition is described. The system developed operates in real time and provides accurate gesture recognition. It operates using a single low resolution camera and operates in Matlab on a conventional PC running Windows XP. The hand shape classifier outlined in this thesis uses transformation invariant subspaces created using Principal Component Analysis (PCA). These subspaces are created from a large vocabulary created in a systematic maimer using computer animation. In recognising dynamic gestures we utilise both hand shape and hand position information; these are two o f the main features used by humans in distinguishing gestures. Hidden Markov Models (HMMs) are trained and employed to recognise this combination of hand shape and hand position features. During the course o f this thesis we have described in detail the inspiration and motivation behind our research and its possible applications. In this work our emphasis is on achieving a high speed system that works in real time with high accuracy

Towards Reliable Real-Time Person Detection

International audienceWe propose a robust real-time person detection system, which aims to serve as solid foundation for developing solutions at an elevated level of reliability. Our belief is that clever handling of input data correlated with efficacious training algorithms are key for obtaining top performance. We introduce a comprehensive training method based on random sampling that compiles optimal classifiers with minimal bias and overfit rate. Building upon recent advances in multi-scale feature computations, our approach attains state-of-the-art accuracy while running at high frame rate

Detecting Nature in Pictures

Com o advento da partilha em grande escala de imagens geo-referenciadas na internet, em portais como o Flickr e Panoramio, existem agora grandes fontes de dados prontas a serem processadas para a extracção de informação útil. A utilização destes dados para a criação de um mapa das áreas naturais e de origem humana do nosso planeta, pode fornecer conhecimento adicional aos decisores políticos responsáveis pela conservação do planeta.O problema de determinar o grau de naturalidade de uma imagem, pré-condição para a criação de tal mapa, pode ser generalizado como um problema de classificação de paisagens. Foram executadas experiências para melhor compreender a aplicabilidade de cada uma das técnicas identificadas para a classificação de paisagens quando aplicada à tarefa de distinguir entre imagens naturais e de origem humana. As suas vantagens e limitações, como os seus requisitos computacionais, são detalhados.Com uma escolha cuidada das técnicas e respectivos parâmetros foi possível construir um classificador capaz de distinguir entre paisagens naturais e de origem humana com elevada precisão, mas também capaz de processar uma grande quantidade de imagens dentro de um espaço de tempo razoável.With the advent of large-scale geo-tagged image sharing on the internet, on websites such as Flickr and Panoramio, there are now large sources of data ready to be mined for useful information. Using this data to automatically create a map of man-made and natural areas of our planet, can provide additional knowledge to decision-makers responsible for world-conservation.The problem of determining the degree of naturalness of an image, required to create such a map, can be generalized as a scene classification task. Experiments were performed to better understand the applicability of each of the identified scene classification techniques to perform the distinction between man-made and natural images. Their advantages and limitations, such as their computational costs, are detailed.With careful selection of techniques and their parameters it was possible to build a classifier capable of distinguishing between natural and man-made scenery with high accuracy and that can also process a large amount of pictures within a reasonable time frame

Detecção de eventos violentos em sequências de vídeos baseada no operador histograma da transformada census

Orientador: Hélio PedriniDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Sistemas de vigilância em sequências de vídeo têm sido amplamente utilizados para o monitoramento de cenas em diversos ambientes, tais como aeroportos, bancos, escolas, indústrias, estações de ônibus e trens, rodovias e lojas. Devido à grande quantidade de informação obtida pelas câmeras de vigilância, o uso de inspeção visual por operadores de câmera se torna uma tarefa cansativa e sujeita a falhas, além de consumir muito tempo. Um desafio é o desenvolvimento de sistemas inteligentes de vigilância capazes de analisar longas sequências de vídeos capturadas por uma rede de câmeras de modo a identificar um determinado comportamento. Neste trabalho, foram propostas e avaliadas diversas técnicas de classificação, tendo como base o operador CENTRIST (Histograma da Transformada Census), no contexto de identificação de eventos violentos em cenas de vídeo. Adicionalmente, foram avaliados outros descritores tradicionais, como HoG (Histograma de Gradientes Orientados), HOF (Histograma do Fluxo Óptico) e descritores extraídos a partir de modelos de aprendizado de máquina profundo pré-treinados. De modo a permitir a avaliação apenas em regiões de interesse presentes nos quadros dos vídeos, técnicas para remoção do fundo da cena. Uma abordagem baseada em janela deslizante foi utilizada para avaliar regiões menores da cena em combinação com um critério de votação. A janela deslizante é então aplicada juntamente com uma filtragem de blocos utilizando fluxo óptico da cena. Para demonstrar a efetividade de nosso método para discriminar violência em cenas de multidões, os resultados obtidos foram comparados com outras abordagens disponíveis na literatura em duas bases de dados públicas (Violence in Crowds e Hockey Fights). A eficácia da combinação entre CENTRIST e HoG foi demonstrada em comparação com a utilização desses operadores individualmente. A combinação desses operadores obteve aproximadamente 88% contra 81% utilizando apenas HoG e 86% utilizando CENTRIST. A partir do refinamento do método proposto, foi identificado que avaliar blocos do quadro com a abordagem de janela deslizante tornou o método mais eficaz. Técnicas para geração de palavras visuais com codificação esparsa, medida de distância com um modelo de misturas Gaussianas e medida de distância entre agrupamentos também foram avaliadas e discutidas. Além disso, também foi avaliado calcular dinamicamente o limiar de votação, o que trouxe resultados melhores em alguns casos. Finalmente, formas de restringir os atores presentes nas cenas utilizando fluxo óptico foram analisadas. Utilizando o método de Otsu para calcular o limiar do fluxo óptico da cena a eficiência supera nossos resultados mais competitivos: 91,46% de acurácia para a base Violence in Crowds e 92,79% para a base Hockey FightsAbstract: Surveillance systems in video sequences have been widely used to monitor scenes in various environments, such as airports, banks, schools, industries, bus and train stations, highways and stores. Due to the large amount of information obtained via surveillance cameras, the use of visual inspection by camera operators becomes a task subject to fatigue and failure, in addition to consuming a lot of time. One challenge is the development of intelligent surveillance systems capable of analyzing long video sequences captured by a network of cameras in order to identify a certain behavior. In this work, we propose and analyze the use of several classification techniques, based on the CENTRIST (Transformation Census Histogram) operator, in the context of identifying violent events in video scenes. Additionally, we evaluated other traditional descriptors, such as HoG (Oriented Gradient Histogram), HOF (Optical Flow Histogram) and descriptors extracted from pre-trained deep machine learning models. In order to allow the evaluation only in regions of interest present in the video frames, we investigated techniques for removing the background from the scene. A sliding window-based approach was used to assess smaller regions of the scene in combination with a voting criterion. The sliding window is then applied along with block filtering using the optical flow of the scene. To demonstrate the effectiveness of our method for discriminating violence in crowd scenes, we compared the results to other approaches available in the literature in two public databases (Violence in Crowds and Hockey Fights). The combination of CENTRIST and HoG was demonstrated in comparison to the use of these operators individually. The combination of both operators obtained approximately 88% against 81% using only HoG and 86% using CENTRIST. From the refinement of the proposed method, we identified that evaluating blocks of the frame with the sliding window-based approach made the method more effective. Techniques for generating a codebook with sparse coding, distance measurement with a Gaussian mixture model and distance measurement between clusters were evaluated and discussed. Also we dynamically calculate the threshold for class voting, which obtained superior results in some cases. Finally, strategies for restricting the actors present in the scenes using optical flow were analyzed. By using the Otsu¿s method to calculate the threshold from the optical flow at the scene, the effectiveness surpasses our most competitive results: 91.46% accuracy for the Violence in Crowds dataset and 92.79% for the Hockey Fights datasetMestradoCiência da ComputaçãoMestre em Ciência da Computaçã

DESIGNING EYE TRACKING ALGORITHM FOR PARTNER-ASSISTED EYE SCANNING KEYBOARD FOR PHYSICALLY CHALLENGED PEOPLE

The proposed research work focuses on building a keyboard through designing an algorithm for eye movement detection using the partner-assisted scanning technique. The study covers all stages of gesture recognition, from data acquisition to eye detection and tracking, and finally classification. With the presence of many techniques to implement the gesture recognition stages, the main objective of this research work is implementing the simple and less expensive technique that produces the best possible results with a high level of accuracy. The results, finally, are compared with similar works done recently to prove the efficiency in implementation of the proposed algorithm. The system starts with the calibration phase, where a face detection algorithm is designed to detect the user‟s face by a trained support vector machine. Then, features are extracted, after which tracking of the eyes is possible by skin-colour segmentation. A couple of other operations were performed. The overall system is a keyboard that works by eye movement, through the partner-assisted scanning technique. A good level of accuracy was achieved, and a couple of alternative methods were implemented and compared. This keyboard adds to the research field, with a new and novel combination of techniques for eye detection and tracking. Also, the developed keyboard helps bridge the gap between physical paralysis and leading a normal life. This system can be used as comparison with other proposed algorithms for eye detection, and might be used as a proof for the efficiency of combining a number of different techniques into one algorithm. Also, it strongly supports the effectiveness of machine learning and appearance-based algorithms