Considerações finais

um peso diferente para cada palavra. A atribuição suave possui alguma semelhança com as técnicas de agrupamento difuso, as quais poderiam ser utilizadas em algoritmos de agrupamento de características.

• Outra proposta é utilizar agrupamento de características na saída do BoW para tentar eliminar a redundância dos dados.

• Utilizar os algoritmos de agrupamento de superpixels (VARGAS et al., 2014; RAUBER et al., 2013) na técnica Pedaços-por-Valor.

• Waveletfaces é uma técnica que apresenta bom desempenho para o reconhecimento de fa- ces (CHIEN; WU, 2002). Waveletfaces foi construído utilizando apenas a função Wavelet de Haar. Porém existe um conjunto de funções Wavelets infinito. É possível explorar o espaço das funções Wavelets, através de Wavelets parametrizáveis (TEWFIK; SINHA; JORGENSEN, 1992). Esta proposta consiste em buscar uma função Wavelet que seja mais adequada para o reconhecimento de faces.

7.4

Considerações finais

Esta pesquisa começou ao se observar o método Waveletfaces (CHIEN; WU, 2002). Constatou- se que, para algumas bases de imagens de face, as imagens poderiam ser bastante reduzidas sem prejudicar o reconhecimento. Para tentar explicar este fato foi proposta a metodologia de Agrupamento de Pixels. Esta metodologia é utilizada para definir métodos que definem regiões para as imagens de face. Então uma característica é extraída para cada região.

Autofaces é o método de referência para extração de características no reconhecimento de faces. Foi necessário aprofundar-se neste método para verificar a relevância dos métodos propostos. Percebeu-se que este também apresenta motivações para a metodologia de Agrupa- mento de Pixels. Com a recente proposta da matriz de covariância fracionária (GAO; ZHOU; PU, 2013) e seus benefícios para o reconhecimento de faces, surgiu a hipótese de adequar esta técnica ao Autofaces. A partir daí foram propostos três métodos que combinam a teoria da

matriz de covariância fracionária e Autofaces.

Esta tese propõe métodos de projeções para extração de características em problemas de Re- conhecimento de Faces. Todos os métodos propostos são projeções lineares não-supervisionadas, como PCA. Alguns métodos fracionários realizam uma transformação não-linear antes da pro- jeção linear dos dados. Acredita-se que esta pesquisa é relevante e atual no seu tema. Espera-se que muitas outras pesquisas possam derivar desta.

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