29 resultados para Facial augmentation membrane


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Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Doutor em Sistemas de Bioengenharia

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Dissertation presented to obtain the Ph.D degree in Biochemistry, Structural Biochemistry

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Dissertation presented to obtain the Ph.D degree in Biology, Microbial Biology

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Dissertation presented to obtain the Ph.D degree in Engineering and Technology Sciences, Biotechnology.

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Dissertation presented to obtain the Ph.D degree in Engineering and Technology Sciences, Chemical Engineering.

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Dissertação para obtenção do Grau de Doutora em Engenharia Química e Bioquímica

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Dissertação para obtenção do Grau de Doutor em Engenharia Química e Bioquímica, Especialidade em Engenharia Bioquímica

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The EM3E Master is an Education Programme supported by the European Commission, the European Membrane Society (EMS), the European Membrane House (EMH), and a large international network of industrial companies, research centers and universities

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The computational power is increasing day by day. Despite that, there are some tasks that are still difficult or even impossible for a computer to perform. For example, while identifying a facial expression is easy for a human, for a computer it is an area in development. To tackle this and similar issues, crowdsourcing has grown as a way to use human computation in a large scale. Crowdsourcing is a novel approach to collect labels in a fast and cheap manner, by sourcing the labels from the crowds. However, these labels lack reliability since annotators are not guaranteed to have any expertise in the field. This fact has led to a new research area where we must create or adapt annotation models to handle these weaklylabeled data. Current techniques explore the annotators’ expertise and the task difficulty as variables that influences labels’ correction. Other specific aspects are also considered by noisy-labels analysis techniques. The main contribution of this thesis is the process to collect reliable crowdsourcing labels for a facial expressions dataset. This process consists in two steps: first, we design our crowdsourcing tasks to collect annotators labels; next, we infer the true label from the collected labels by applying state-of-art crowdsourcing algorithms. At the same time, a facial expression dataset is created, containing 40.000 images and respective labels. At the end, we publish the resulting dataset.

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The EM3E Master is an Education Programme supported by the European Commission, the European Membrane Society (EMS), the European Membrane House (EMH), and a large international network of industrial companies, research centres and universities (http://www.em3e.eu)