Learned Features are better for Ethnicity Classification

TL;DR

This paper introduces a novel ethnicity classification method using features learned by a pre-trained CNN combined with an SVM classifier, achieving high accuracy across multiple facial databases.

Contribution

The paper presents a new approach leveraging deep learned features for ethnicity classification, outperforming traditional hand-crafted feature methods.

Findings

Achieved over 98% accuracy in ethnicity classification

Robust to variations in expressions and illumination

Effective across multiple facial databases

Abstract

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.