Comparação entre algoritmo genético, rede neural artificial e análise de componentes principais no reconhecimento de faces

• Ano de defesa: 2013
• Autor(a) principal: Arruda, Benedito Alencar de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Engenharia Elétrica; Engenharias; UFU
• Palavras-chave em Português: Reconhecimento de faces; Rede neural artificial; Análise de componentes principais; Algoritmo genético; Redes neurais (Computação); Reconhecimento de padões; Face recognition; Artificial neural networks; Principal components analysis; Genetic algorithm; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14316; https://doi.org/10.14393/ufu.te.2013.6

The face recognition has been shown to be an important technique in automatic identification of persons. It is a biometric image coding characterized by exploring selfsimilarity present in digital images and whose computational effort is significant and has required dedication of researchers in order to increasingly enhance the efficiency of the process. This thesis was presented the state of the art face recognition systems citing different forms of applications and work done by some researchers. It was also shown the methodology used by the classifiers Genetic Algorithm (GA), Artificial Neural Network (ANN) and Principal Component Analysis (PCA). The aim of this study was to test these classifiers for face recognition by analyzing the percentage of accuracy and processing time. The reason to use the GA is that the PCA, traditionally used in such cases, is very slow and the high computational cost, making it impractical in some applications, especially when the database is large images. In tests performed in this study were used the databases of two human photography files, the Olivetti Research Laboratory database today faces the British University of Cambridge (ORL) and Face Recognition Data, University of Essex, UK (UK). The accuracy rate obtained with GA, ANN and PCA were higher than those obtained by the studies reviewed.