An Integrated Approach to Classify Gender and Ethnicity

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.