DeepEthnic: Multi-Label Ethnic Classification from Face Images
Computer Vision and Pattern Recognition
2019-12-09 v1 Machine Learning
Neural and Evolutionary Computing
Machine Learning
Abstract
Ethnic group classification is a well-researched problem, which has been pursued mainly during the past two decades via traditional approaches of image processing and machine learning. In this paper, we propose a method of classifying an image face into an ethnic group by applying transfer learning from a previously trained classification network for large-scale data recognition. Our proposed method yields state-of-the-art success rates of 99.02%, 99.76%, 99.2%, and 96.7%, respectively, for the four ethnic groups: African, Asian, Caucasian, and Indian.
Cite
@article{arxiv.1912.02983,
title = {DeepEthnic: Multi-Label Ethnic Classification from Face Images},
author = {Katia Huri and Eli David and Nathan S. Netanyahu},
journal= {arXiv preprint arXiv:1912.02983},
year = {2019}
}