A bag of features approach for human attribute analysis on face images

• Ano de defesa: 2019
• Autor(a) principal: Araujo, Rafael Will Macêdo de
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
• Palavras-chave em Português: Aprendizagem por dicionário; Bag-of-visual-words model; Classificação de gênero e etnia; Dictionary learning; Face image processing; Gender and ethnicity classification; Modelo bag-of-words visual; Processamento de imagens de faces
• Link de acesso: http://www.teses.usp.br/teses/disponiveis/45/45134/tde-07102019-213618/

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.