  Ethnicity is a key demographic attribute of human beings and it plays a vital
role in automatic facial recognition and have extensive real world applications
such as Human Computer Interaction (HCI); demographic based classification;
biometric based recognition; security and defense to name a few. In this paper
we present a novel approach for extracting ethnicity from the facial images.
The proposed method makes use of a pre trained Convolutional Neural Network
(CNN) to extract the features and then Support Vector Machine (SVM) with linear
kernel is used as a classifier. This technique uses translational invariant
hierarchical features learned by the network, in contrast to previous works,
which use hand crafted features such as Local Binary Pattern (LBP); Gabor etc.
Thorough experiments are presented on ten different facial databases which
strongly suggest that our approach is robust to different expressions and
illuminations conditions. Here we consider ethnicity classification as a three
class problem including Asian, African-American and Caucasian. Average
classification accuracy over all databases is 98.28%, 99.66% and 99.05% for
Asian, African-American and Caucasian respectively.
