An Integrated Approach to Classify Gender and Ethnicity
Date
2016-10-28
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Faces express many social indications, including
gender, ethnicity, age, expression and identity, most of them have
drawn thriving attention from various research communities, for
instance neuroscience, computer science and psychology. In this
paper, we propose a new approach to classify gender and
ethnicity by merging both texture and shape features extracted
from face images. Gabor filter is used to extract the texture
features and histogram of oriented gradients (HOG) is used to
extract the shape features from face images. In order to achieve
higher performance we combined both texture and shape
features. After combining, the size of feature vector obtained is in
a high dimension, to decrease the dimensionality Kernel PCA has
been implemented. Finally, to classify the gender and ethnicity
we used Support Vector Machine. The experimental result shows
the effectiveness of proposed framework.
Description
Keywords
Gender Recognition, Ethnicity Recognition, Gabor filter, Histogram of oriented gradients, Kernel PCA, Support Vector Machine.
Citation
ICISET2016-ID-6