Detecção de maquiagem facial por meio de CMYK e redes neurais

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dc.contributor.advisorSilveira, Ismar Frango
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/3894359521286830por
dc.contributor.authorBertacchi, Marcello Guariento
dc.creator.Latteshttp://lattes.cnpq.br/3274014160668400por
dc.date.accessioned2018-05-04T15:59:12Z
dc.date.accessioned2020-05-28T18:08:54Z
dc.date.available2020-05-28T18:08:54Z
dc.date.issued2018-02-16
dc.description.abstractInitially, facial feature recognition was only used intuitively, which means that one individual recognized another by certain characteristics relevant for their identification. Time passed, and with technological advancement, other methods were created for this purpose. However, the addition of artificial characteristics could have a negative influence in the process of facial recognition. Hence the choice of the cosmetic application field, with the purpose of exploring in more details both the effects in recognition as well as the process of detection of facial makeup. For this purpose, the color model CMYK was chosen due to its satisfactory performance in skin detection. The objective of this work is to emphasize the feasibility of applying the color model CMYK in Computational Vision procedures and Image Analysis, in comparisson to another model widely used, which is the HSV. For the makeup classification process, it was chosen a variant of Artificial Neural Networks known as Neural Network Convolutional, which is based on the visual cortex of cats. First, it was proved the negative influence of makeup in face recognition, through the LBP descriptor. In sequence, six neural networks were trained to detect makeup, achieving an accuracy of 97 percentage points on the eye region, 95 points percent on the face and 80 percentage points on the lips, in CMYK’s model, and 91 percentage points on the eye region, 92 points percent on the face and 73 percentage points on the lips, in HSV’s model. Consequently, CMYK was proven to be a color space that deserves attention in the fields of Makeup and Computer Vision.eng
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superiorpor
dc.formatapplication/pdf*
dc.identifier.citationBERTACCHI, Marcello Guariento. Detecção de maquiagem facial por meio de CMYK e redes neurais. 2018. 141 f. Dissertação( Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24475
dc.keywordsmakeup detectioneng
dc.keywordscomputer visioneng
dc.keywordsCMYKeng
dc.keywordsneural networkseng
dc.keywordsHSVeng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectdetecção de maquiagempor
dc.subjectvisão computacionalpor
dc.subjectCMYKpor
dc.subjectredes neuraispor
dc.subjectHSVpor
dc.subject.cnpqCNPQ::ENGENHARIASpor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/16585/MARCELLO%20GUARIENTO%20BERTACCHI.pdf.jpg*
dc.titleDetecção de maquiagem facial por meio de CMYK e redes neuraispor
dc.typeDissertaçãopor
local.contributor.board1Silva, Luciano
local.contributor.board1Latteshttp://lattes.cnpq.br/7514305376858192por
local.contributor.board2Marques, Fátima de Lourdes dos Santos Nunes
local.contributor.board2Latteshttp://lattes.cnpq.br/8626964624628522por
local.publisher.countryBrasilpor
local.publisher.departmentFaculdade de Computação e Informática (FCI)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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