REDE NEURAL PROFUNDA DE MÚLTIPLAS ENTRADAS COM DADOS 3D APLICADOS AO RECONHECIMENTO FACIAL
Palavras-chave:
Reconhecimento facial, pix2vertex, SIFTResumo
Neste trabalho é proposta uma metodologia utilizando rede neural multientrada para realizar o reconhecimento facial de indivíduos a partir das características 3D extraídas de imagens frontais. Para a extração das características 3D as imagens foram inicialmente submetidas à rede pix2vertex para realizar a reconstrução 3D da geometria facial de cada indivíduo. Após a reconstrução 3D foram extraídos 275 pontos, contendo as seguintes informações: coordenadas x, y e z, e os descritores de pontos chave do algoritmo SIFT (Scale Invariant Feature Transform). E por fim, essas informações são processadas em uma rede neural artificial de múltiplas entradas para a previsão da classificação de cada indivíduo. A avaliação dos resultados mostra que a rede foi capaz de classificar corretamente os indivíduos com uma precisão de 95,79% no conjunto de validação.
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