Decomposição aleatória de matrizes aplicada ao reconhecimento de faces

• Autor(a) principal: Amorim, Mauro de
• Data de Publicação: 2013
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações da UERJ
• Texto Completo: http://www.bdtd.uerj.br/handle/1/13763
• topic: Singular value decomposition; Principal component analysis; Eigenfaces; Face recognition; Approximate matrix decompositions; Stochastic methods; Decomposição em valores singulares; Análise de componentes principais; Autofaces; Reconhecimento de faces; Decomposição aproximada de matrizes; Métodos estocásticos; CNPQ::CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA::PROBABILIDADE E ESTATISTICA APLICADAS

Stochastic methods offer a powerful tool for performing data compression and decomposition of matrices. These methods use random sampling to identify a subspace that captures the range of a matrix in an approximate way, preserving a part of its essential information. These approaches compress the information enabling the resolution of practical problems efficiently. This work computes a singular value decomposition (SVD) of a matrix using stochastic techniques. This random SVD is employed in the task of face recognition. The face recognition is based on the projection of images of faces on a feature space that best describes the variation of known image faces. These features are known as eigenfaces because they are the eigenvectors of a matrix constructed from a set of faces. This projection characterizes an individual face by a weighted sum of eigenfaces. The task of recognizing a new face is to compare the weights of its projection with the projection of the weights of known individuals. The principal components analysis (PCA) is a widely used method for determining the eigenfaces. This provides the greatest variability eigenfaces representing information from a set of faces. In this dissertation we discuss the quality of eigenfaces obtained by a random SVD (which are the left singular vectors of a matrix containing the images) by comparing the similarity with eigenfaces obtained by PCA. We use two databases of images, with different sizes and various random sampling applied on the matrix containing the images.