47 research outputs found

    Facial Expression Recognition

    Get PDF

    Video-based infant discomfort detection

    Get PDF

    Reconhecimento de expressões faciais em neonatos

    Get PDF
    Orientador: Profa Dra Olga R. P. BellonDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 30/10/2019Inclui referências: p. 46-51Área de concentração: Ciência da ComputaçãoResumo: A avaliação de dor é uma tarefa difícil e complexa, que é particularmente importante para recém-nascidos, que não conseguem verbaliza-la de maneira adequada e são vulneráveis a danos cerebrais decorrentes do não tratamento da dor. As ferramentas utilizadas no ambiente clínico para auxiliar na avaliação de dor requerem treinamento dos profissionais de saúde que irão utilizá-las, e seu uso é afetado pelo viés no reconhecimento da dor de cada indivíduo. Por essa razão, esforços tem sido colocados em automatizar essa tarefa, e uma das maneiras de fazê-lo é analisando a expressão facial do neonato, uma vez que esta é comprovadamente correlacionada à dor. Nessa dissertação, as diferenças entre os principais trabalhos em reconhecimento automático de expressão facial de neonatos são apresentadas, examinando os métodos utilizados, bases de dados e performances dos sistemas. Com isso em mente, testamos os principais métodos utilizados com objetivo de comparar suas performances mais a fundo. Esse estudo também avança o entendimento da base de dados COPE, a única base de dados de expressão facial de neonatos publicamente disponível. Conduzimos testes com métodos off the shelf para detecção de face, e em 54% das imagens nenhuma face foi detectada, reforçando a necessidade do desenvolvimento de sistemas específicos para recém-nascidos ou mais robustos à mudanças de público. Desde a publicação da base COPE em 2005, avanços significativos foram alcançados na área de processamento de imagens, e por essa razão comparamos métodos clássicos de extração de características em processamento de imagens com características provenientes de redes neurais convolucionais (CNNs), que são consideradas estado da arte para a maioria das aplicações de visão computacional. Um delta de 19% foi observado entre os filtros de gabor (melhor dos métodos clássicos) e características da ResNet50 (melhor das CNNs). Também testamos a robustez dos métodos a ruído, um fator importante em problemas de visão computacional onde devem ser considerados cenários da vida real. Para os métodos clássicos, foi observado um delta menor na performance entre cenários limpos e ruidosos, mas de maneira geral a performance foi pior que das CNNs. Em adição, estressando a performance das CNNs, testamos quais camadas produziriam melhor performance, na tentativa de verificar se camadas mais rasas poderiam ter desempenho igual ou melhor que camadas mais profundas, o que significaria menor custo computacional. Os resultados mostraram melhores resultados utilizando as camadas mais profundas. De maneira geral, estudando a literatura da área notamos uma tendência na utilização de métricas enviesadas, como acurácia, em um campo onde uma visão mais completa de performance de modelos deveria ser utilizada, por se tratar de um público tão vulnerável. Por fim, também observamos uma dificuldade no acesso as bases da literatura. Nossos esforços reforçam o potencial da utilização de métodos de visão computacional, porém fora limitados à base de dados utilizada. Palavras-chave: Expressões faciais, avaliação de dor, visão computacionalAbstract: Pain evaluation is a difficult and complex task, that is particularly important for newborns, who cannot verbalize it properly and are vulnerable to cerebral damage due to untreated pain. The current pain assessment tools used in clinical settings require extensive training for the caregivers and can be affected by each individual's bias towards pain recognition. For this reason, efforts have been made to automate this task, and one of the ways to do so is analyzing the newborn's facial expression, that has been proved to correlate with pain. In this dissertation, the differences among the most prominent works in automatic neonatal facial expression recognition were outlined, examining methods used, databases and final performance. With this in mind, we tested main methods used to compare their performances more in depth. This study also advances the understanding of the COPE database, the only publicly available newborn facial expression database. We conducted a test with off the shelf methods for face detection, and found that in 54% of the images, no face was found, reinforcing the need to develop either tailored applications or more robust ones. Since the COPE database was published, in 2005, significant advances in image processing have been made, and for this reason, we compared classical image processing feature extraction methods with Convolutional Neural Networks (CNNs), that are considered to be state of the art for most computer vision problems. We saw a difference of 19% in recall when using gabor filters (best of classical methods) and then the ResNet50 features (best of CNNs). We also tested the methods in regards to robustness to image noise, an important factor for computer vision problems when real world scenarios are considered. We found that image processing methods had a smaller delta in performance from clean to noisy scenarios, but had overall poor performance. In addition, stressing the CNNs performance, we also studied which layers yielded best performance in order to verify if shallow layers could produce the same results as deeper ones for this application, meaning less computational cost, but our test showed superior performance in deeper layers. Overall, studying the literature we noticed a tendency to use biased metrics, such as accuracy, in a field where a more complete view of model performance should be used. Moreover, we also found it very difficult to access data for this field. Our findings reinforce the potential of more complex computer vision methods, but are limited to the dataset that was used. Keywords: Facial expression, pain assessment, computer visio

    gLOP: A Cleaner Dirty Model for Multitask Learning

    Get PDF
    Multitask learning (MTL) was originally defined by Caruana (1997) as "an approach to inductive transfer that improves learning for one task by using the information contained in the training signals of other related tasks". In the linear model setting this is often realized as joint feature selection across tasks, where features (but not necessarily coefficient values) are shared across tasks. In later work related to MTL Jalali (2010) observed that sharing all features across all tasks is too restrictive in some cases, as commonly used composite absolute penalties (like the l(1,∞) norm) encourage not only common feature selection but also common parameter values between settings. Because of this, Jalali proposed an alternative "dirty model" that can leverage shared features even in the case where not all features are shared across settings. The dirty model decomposes the coefficient matrix Θ into a row-sparse matrix B and an elementwise sparse matrix S in order to better capture structural differences between tasks. Multitask learning problems arise in many contexts, and one of the most pertinent of these is healthcare applications in which we must use data from multiple patients to learn a common predictive model. Often it is impossible to gather enough data from any one patient to accurately train a full predictive model for that patient. Additionally, learning in this context is complicated by the presence of individual differences between patients as well as population-wide effects common to most patients, leading to the need for a dirty model. Two additional challenges for methods applied in the healthcare setting include the need for scalability so that the model can work with big data, and the need for interpretable models. While Jalali gives us a dirty model, this method does not scale as well as many other commonly used methods like the Lasso, and does not have a clean interpretation. This is particularly true in the healthcare domain, as this model does not allow us to interpret coefficients in relation to all settings. Because B coefficients in the dirty model paradigm are not required to be the same for all settings for a particular feature, departures from the global model may be captured in B or S leading to ambiguity in interpreting potential main effects. We propose a "cleaner" dirty model gLOP (global/LOcal Penalty) that is capable of representing global effects between settings as well as local setting-specific effects, much like the ANalysis Of VAriance (ANOVA) test in inferential statistics. However, the goal of the ANOVA is not to build an accurate predictive model, but to identify coefficients that are non-zero at a given level of statistical significance. By combining the dirty model's decomposed Θ matrix and the underlying concept behind the ANOVA, we get the best of both worlds: an interpretable predictive model that can accurately recover the underlying structure of a given problem. gLOP is structured as a coordinate minimization problem which decomposes Θ into a global vector of coefficients g and a matrix of local setting-specific coefficients L. At each step, g is updated using the standard Lasso paradigm applied to the composite global design matrix in which the design matrices from each setting are concatenated vertically. In contrast, L is updated at each step using the standard Lasso paradigm applied separately to each setting. Another significant advantage of our model gLOP in comparison to previous dirty models is the out-of-the-box use of standard Lasso implementations instead of less frequently implemented CAP family penalties such as the l(1,∞) norm. Additionally, gLOP has a significant advantage in lowered computational time demands as it takes larger steps towards the global optimum at each iteration. We present experimental results comparing both the runtime and structure recovered by gLOP to Jalali's dirty model

    Biometrics

    Get PDF
    Biometrics-Unique and Diverse Applications in Nature, Science, and Technology provides a unique sampling of the diverse ways in which biometrics is integrated into our lives and our technology. From time immemorial, we as humans have been intrigued by, perplexed by, and entertained by observing and analyzing ourselves and the natural world around us. Science and technology have evolved to a point where we can empirically record a measure of a biological or behavioral feature and use it for recognizing patterns, trends, and or discrete phenomena, such as individuals' and this is what biometrics is all about. Understanding some of the ways in which we use biometrics and for what specific purposes is what this book is all about

    Diseño de un modelo para la clasificación de dolor postquirúrgico en pacientes pediátricos no-comunicativos del Hospital Universitario San Vicente Fundación - Medellín

    Get PDF
    RESUMEN: La evaluación del nivel de dolor postoperatorio (POP) percibido en los pacientes ha ido mejorando en los ámbitos hospitalarios, es tanto así, que se han desarrollado protocolos de control del dolor y escalas de evaluación del mismo. Sin embargo, la evaluación del dolor en la población infantil presenta dificultades en torno a la incapacidad de ellos para describir verbalmente la ubicación, duración e intensidad de la experiencia dolorosa. A pesar de haber escalas de evaluación de dolor dirigidas especialmente a los niños no-comunicativos, existen falencias en cuanto a la subjetividad del personal quien evalúa el dolor

    Development of Markerless Systems for Automatic Analysis of Movements and Facial Expressions: Applications in Neurophysiology

    Get PDF
    This project is focused on the development of markerless methods for studying facial expressions and movements in neurology, focusing on Parkinson’s disease (PD) and disorders of consciousness (DOC). PD is a neurodegenerative illness that affects around 2% of the population over 65 years old. Impairments of voice/speech are among the main signs of PD. This set of impairments is called hypokinetic dysarthria, because of the reduced range of movements involved in speech. This reduction can be visible also in other facial muscles, leading to a hypomimia. Despite the high percentage of patients that suffer from dysarthria and hypomimia, only a few of them undergo speech therapy with the aim to improve the dynamic of articulatory/facial movements. The main reason is the lack of low cost methodologies that could be implemented at home. DOC after coma are Vegetative State (VS), characterized by the absence of self-awareness and awareness of the environment, and Minimally Conscious State (MCS), in which certain behaviors are sufficiently reproducible to be distinguished from reflex responses. The differential diagnosis between VS and MCS can be hard and prone to a high rate of misdiagnosis (~40%). This differential diagnosis is mainly based on neuro-behavioral scales. A key role to plan the rehabilitation in DOC patients is played by the first diagnosis after coma. In fact, MCS patients are more prone to a consciousness recovery than VS patients. Concerning PD the aim is the development of contactless systems that could be used to study symptoms related to speech and facial movements/expressions. The methods proposed here, based on acoustical analysis and video processing techniques could support patients during speech therapy also at home. Concerning DOC patients the project is focused on the assessment of reflex and cognitive responses to standardized stimuli. This would allow objectifying the perceptual analysis performed by clinicians
    corecore