Estudo dimensional de características aplicadas à leitura labial automática

• Ano de defesa: 2018
• Autor(a) principal: Madureira, Fillipe Levi Guedes
• Orientador(a): Montalvão Filho, Jugurta Rosa
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Programa de Pós-Graduação: Pós-Graduação em Engenharia Elétrica
• Palavras-chave em Português: Engenharia elétrica; Surdos; Comunicação oral; Sistemas de reconhecimento de padrões; Dimensão intrínseca; Extração de características; Leitura labial; Hidden Markov Model (HMM)
• Palavras-chave em Inglês: Intrinsic dimension; Feature extraction; Lip-reading
• Área do conhecimento CNPq: ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://ri.ufs.br/jspui/handle/riufs/9567

This work is a study of the relationship between the intrinsic dimension of feature vectors applied to the classification of video signals in order to perform lip reading. In pattern recognition tasks, the extraction of relevant features is crucial for a good performance of the classifiers. The starting point of this work was the reproduction of the work of J.R. Movellan [1], which classifies lips gestures with HMM using only the video signal from the Tulips1 database. The database consists of videos of volunteers’ mouths while they utter the first 4 numerals in English. The original work uses feature vectors of high dimensionality in relation to the size of the database. Consequently, the adjustment of HMM classifiers has become problematic and the maximum accuracy was only 66.67%. Alternative strategies for feature extraction and classification schemes were proposed in order to analyze the influence of the intrinsic dimension in the performance of classifiers. The best solution, in terms of results, achieved an accuracy of approximately 83%.