Reconhecimento facial utilizando análise de componentes principais e algoritmos genéticos em imagens segmentadas

• Ano de defesa: 2012
• Autor(a) principal: Medeiros, Luciano Xavier
• 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: PCA; Reconhecimento facial; Regiões faciais; Segmentação; Computação; Reconhecimento do rosto humano (Computação); Algoritmos genéticos; Genetic algorithms; Face recognition; Face regions and segmentation; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: https://repositorio.ufu.br/handle/123456789/14312; https://doi.org/10.14393/ufu.te.2012.64

A person can be remembered or identified by various physical characteristics. During childhood, a child learns to identify people by voice, the smell, but probably the most remarkable characteristic of a person is face. Due to the fact that looking at the face of a person more information is collected almost simultaneously, such as eye color, mouth shape, hair texture, skin color, etc.. With a larger amount of information, the probability of incorrectly identifying a person is lower, ie it is more precise to recognize a person from the image of his face than from a voice sample. The person recognition from the image face has always been an important task. This recognition may have several objectives such as identifying a criminal, to allow people access to restricted areas, etc.. Thereforewith the improvement of computers various facial recognition techniques were developed, including techniques that use PCA (Principal Component Analysis). The purpose of this research is develop some methods of face recognition that uses less computational resources and are faster. Based on PCA method, two techniques were developed, the methods Segmented PCA and Selective PCA , which had the highest recognition rates and processing times smaller than Advanced PCA. Other three recognition techniques also developed in this thesis and theses techniques using Genetic Algorithms which also performed better than Advanced PCA. The techniques developed in this thesis can also perform facial recognition from facial regions (forehead, eyes, nose, mouth and chin), it is not always the whole picture of a person s face is available to perform the identification, for example, images obtained from security cameras where a burglar is hooded and the camera can record only the eye region.