Open AccessGender Classification using Central Fibonacci Weighted Neighborhood Pattern Flooding Binary Matrix (CFWNP_FBM) Shape Primitive Features
Author(s) -
Pratap Reddy,
AUTHOR_ID,
G. R. Sakthidharan,
S. Suguna,
J. Mannar Mannan,
P. V. Narasimha Rao,
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AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.f9284.088619
Subject(s) - fibonacci number , pattern recognition (psychology) , artificial intelligence , computer science , support vector machine , pixel , biometrics , local binary patterns , image (mathematics) , binary number , range (aeronautics) , wavelet , computer vision , mathematics , materials science , arithmetic , discrete mathematics , composite material , histogram
Gender Classification from facial images is an open research area with wide range of computer vision applications like security, biometrics and human computer interaction applications. In the proposed method the LL band image of facial image is obtained by using wavelet then on this image Fibonacci Weighted Neighborhood Central pixel Flood binary Matrix is computed and then shape features are evaluated. SVM method uses these shape features for gender classification. The proposed approach has been experimented on FG NET database. The experimental results has shown the more accuracy compared to with other existing methods.