Learned Features are better for Ethnicity Classification
This work addresses ethnicity classification for applications in facial recognition and security, but it is incremental as it applies existing deep learning methods to this specific task.
The paper tackled ethnicity classification from facial images by using a pre-trained CNN for feature extraction and an SVM classifier, achieving average accuracies of 98.28% for Asian, 99.66% for African-American, and 99.05% for Caucasian across ten databases.
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.