• Resumo

    Evoluindo Redes Neurais Convolucionais na Detecção de Emoções Usando Micro AGs

    Data de publicação: 13/07/2022

    The Deep-emotive v.1 is a CNN that recognizes emotions by the
    human face’s pictures. In this context, the CNN’s structure creation
    depends on several hyperparameters, which impact the results
    positively or negatively. The Genetic Algorithm implementation
    allows us to explore the search space of these hyperparameters
    to find the best architecture for solving the problem. The defined
    search space is formed by the combination of both the number
    of convolutional layers and the fully connected ones, the number
    of filters for each layer, the size of filters, the subsampling type,
    and the number of nodes in the fully connected layer. This paper

    proposes to improve the Deep-Emotive network with the imple-
    mentation of Convolutional Neural Networks (CNNs) architectures

    using Genetic Algorithms. The FER-2013 dataset was chosen to
    classify seven emotions by images of facial expressions, as it had

    the worst performance in the first version of the network, reach-
    ing an accuracy of 60.71%. This dataset has images with common

    problems for computer vision algorithms, such as occlusion, im-
    balance, perspective, noises, as well as images that do not exist

    in the context of emotions. The experiment’s results indicate that
    the proposed approach can generate a CNN architecture with an
    accuracy of 63,84% in the train set and 62,39% in the validation
    set. Despite a low-performance rate, the experiments indicate that
    the algorithm can generate more adapted individuals who have
    already overcome the performance achieved by the first version of
    the network defined empirically. Thus, results show potential for
    exploitation in environments with more computational resources.

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