Reconhecimento de pessoas pela marcha usando redução de dimensionalidade de contornos no domínio da frequência

• Ano de defesa: 2016
• Autor(a) principal: Mendes, Wender Cabral
• Orientador(a): Laureano, Gustavo Teodoro
• Banca de defesa: Laureano, Gustavo Teodoro, Soares, Anderson da Silva, Coelho, Clarimar José, Costa, Ronaldo Martins da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Marcha humana; Biometria; Baseado em silhueta; Reconhecimento da marcha humana; Redução de dimensionalidade; Domínio da frequência
• Palavras-chave em Inglês: Human gait; Biometry; Silhuette based; Gait recognition; Dimensionality reduction; Frequency domain
• Área do conhecimento CNPq: CIENCIA DA COMPUTACAO::MATEMATICA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/5931

Gait recognition via computer vision attracted increasing interest for its noninvasive characteristic and mainly for your advantage of recognizing people at distance. Recognition is performing extracting features included in gait, this features are extracted from images sequence of people walking. The main challenges of gait recognition is to extract characteristics with unique information for each person, in additional, the use of accessories and clothes difficult the feature extraction process. This paper proposes a gait recognition method using information of people’s contours transformed in domain frequence by Discrete Fourier Transform. A lot of data are generated from the contours, thereby, three different techniques for dimensionality reduction CDA (Class Discrimination Ability), PCA (Principal Component Analysis) and PLS (Partial Least Squares) are employed to reduce the dimensionality of data and generate characteristics that are relevant to the recongnition system. Two classifiers, KNN (K-Nearest Neighbor) and LDA (Linear Discriminant Analysis) classify the characteristics that are returned by the dimensionality reduction methods. The accuracy are achieved by the combination of the dimensionality reduction methods and classifiers, the highest accuracy was 92:67%, which was achieved with the combination between the LDA and PCA (LDAPCA). Therefore, the results show that the information contained in the contours of silhouette are discriminant to recognize people by their gait.