Reconhecimento de pessoas pela marcha usando redução de dimensionalidade de contornos no domínio da frequência
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2016-03-31
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Universidade Federal de Goiás
Resumo
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.
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Citação
MENDES, Wender Cabral. Reconhecimento de pessoas pela marcha usando redução de dimensionalidade de contornos no domínio da frequência. 2016. 57 f. Dissertação (Mestrado em Ciência da Computação) -Universidade Federal de Goiás, Goiânia, 2016.