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dc.contributorSalamí San Juan, Esther
dc.contributor.authorSaez De La Pascua, Adrian
dc.contributor.otherUniversitat Politècnica de Catalunya. Arquitectura de Computadors
dc.description.abstractBoth Machine learning and Deep learning are two concepts that are present in many areas, in health for medical diagnoses, as well as in marketing when segmenting the market or as in most social networks. In this project, we will see what they can offer at the level of recognition of facial expressions in photos. At present, there is infinity of data that is not used since the volume of data is so large that people are not able to process such quantity. The Deep learning and the Machine learning offer several mechanisms to be able to process this data, order them so that they can obtain information and take predictive models to be able to apply them to other data. The recognition of facial expressions is a part of artificial intelligence whose main objective is to recognize basic forms of affective expression that appear on the faces of people. However, can machine learning and deep learning techniques offer to be able to recognize these facial expressions sufficiently efficiently? In this project, we will apply these mechanisms offered by these two technologies to recognize feelings or emotions in people. To do this, we will first process and obtain the information we want from a database. This database is a series of images of people showing different feelings. Once obtained these data we will see that although the two techniques are promising, in Deep learning will obtain better results in precision and more robustness in the code.
dc.publisherUniversitat Politècnica de Catalunya
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica
dc.subject.lcshDeep learning
dc.subject.otherDeep learning
dc.subject.otherAprendizaje automatico
dc.subject.otherDeteccion de caras
dc.subject.otherMarcas faciales
dc.subject.otherExpresiones faciales
dc.titleDeep learning para el reconocimiento facial de emociones basicas
dc.typeBachelor thesis
dc.rights.accessOpen Access
dc.audience.mediatorEscola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels

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