Modelos matemáticos para redução do espectro provável e detecção de tons de pele humana em imagens coloridas representadas nos espaços de cores RGB e HSV

Detalhes bibliográficos
Ano de defesa: 2015
Autor(a) principal: Feitosa, Rafael Divino Ferreira lattes
Orientador(a): Oliveira, Leandro Luís Galdino lattes
Banca de defesa: Oliveira, Leandro Luís Galdino de, Soares, Fabrízzio Alphonsus Alves de Melo Nunes, Silva, Solange da
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Goiás
Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
Departamento: Instituto de Informática - INF (RG)
País: Brasil
Palavras-chave em Português:
RGB
HSV
Palavras-chave em Inglês:
Área do conhecimento CNPq:
Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/4756
Resumo: Skin detection techniques are widely applied to locate and to track parts of the human body with the objective of posterior recognition, having received great attention in recent years in the development of research in reason to the innumerable possible applications with the detection and tracking of faces, identification of naked people, identification of hand movements, among others. The present work proposed the construction of mathematical models for the detection of human skin tones such as, white, yellow, brown and black in digital color images in the RGB and HSV color spaces. Using a set of human skin tone samples, mathematical models were constructed describing how the variables of each color pixel in the RGB and HSV systems interrelate. To understand the answer of the proposed system, the mechanistic model was chosen, dividing it into components, observing the behavior of each part and the interactions that occurred between them. The proposed RGB filter reached a 98.3657% reduction index of the spectrum, classifying only 1.6343% (253,159 tones) as possible skin tones and the HSV model reduced the likely spectrum to 2.5352% (94,030 tones), discarding 97.4648% of the colors as candidates for human skin tones. When the proposed filters, were applied to the reduction of the probable range of human skin tones, well-defined bands in the geometric representation of the color spaces were selected. The experimental validation of the effectiveness of the RGB model showed that the proposed filter has conservative characteristics in the detection of skin, mistakenly classifying as skin only 6.7163% of the sample space. The proposed RGB filter has low sensitivity of 61.0831% and high specificity of 95.2769% in the detection of human skin in digital images. The HSV model had rates of (54,6333%) low sensitivity and (92,6390%) high specificity, considered low when compared to the performance of the other algorithms.