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Doctoral Thesis
DOI
Document
Author
Full name
Rafael Will Macêdo de Araujo
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2019
Supervisor
Committee
Hirata Junior, Roberto (President)
Gomes, David Menotti
Marana, Aparecido Nilceu
Miranda, Paulo Andre Vechiatto de
Rakotomamonjy, Alain Ndimby Heritsimba
Title in English
A bag of features approach for human attribute analysis on face images
Keywords in English
Bag-of-visual-words model
Dictionary learning
Face image processing
Gender and ethnicity classification
Abstract in English
Computer Vision researchers are constantly challenged with questions that are motivated by real applications. One of these questions is whether a computer program could distinguish groups of people based on their geographical ancestry, using only frontal images of their faces. The advances in this research area in the last ten years show that the answer to that question is affirmative. Several papers address this problem by applying methods such as Local Binary Patterns (LBP), raw pixel values, Principal or Independent Component Analysis (PCA/ICA), Gabor filters, Biologically Inspired Features (BIF), and more recently, Convolution Neural Networks (CNN). In this work we propose to combine the Bag-of-Visual-Words model with new dictionary learning techniques and a new spatial structure approach for image features. An extensive set of experiments has been performed using two of the largest face image databases available (MORPH-II and FERET), reaching very competitive results for gender and ethnicity recognition, while using a considerable small set of images for training.
Title in Portuguese
Uma abordagem "bag of features" para análise de atributos humanos em imagens de faces
Keywords in Portuguese
Aprendizagem por dicionário
Classificação de gênero e etnia
Modelo bag-of-words visual
Processamento de imagens de faces
Abstract in Portuguese
Pesquisadores de visão computacional são constantemente desafiados com perguntas motivadas por aplicações reais. Uma dessas questões é se um programa de computador poderia distinguir grupos de pessoas com base em sua ascendência geográfica, usando apenas imagens frontais de seus rostos. Os avanços nesta área de pesquisa nos últimos dez anos mostram que a resposta a essa pergunta é afirmativa. Vários artigos abordam esse problema aplicando métodos como Padrões Binários Locais (LBP), valores de pixels brutos, Análise de Componentes Principais ou Independentes (PCA/ICA), filtros de Gabor, Características Biologicamente Inspiradas (BIF) e, mais recentemente, Redes Neurais Convolucionais (CNN). Neste trabalho propomos combinar o modelo "bag-of-words" visual com novas técnicas de aprendizagem por dicionário e uma nova abordagem de estrutura espacial para características da imagem. Um extenso conjunto de experimentos foi realizado usando dois dos maiores bancos de dados de imagens faciais disponíveis (MORPH-II e FERET), alcançando resultados muito competitivos para reconhecimento de gênero e etnia, ao passo que utiliza um conjunto consideravelmente pequeno de imagens para treinamento.
 
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Publishing Date
2019-10-08
 
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