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Facial Ethnicity Classification with Deep Convolutional Neural Networks

  • Wei Wang
  • Feixiang He
  • Qijun ZhaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9967)

Abstract

As an important attribute of human beings, ethnicity plays a very basic and crucial role in biometric recognition. In this paper, we propose a novel approach to solve the problem of ethnicity classification. Existing methods of ethnicity classification normally consist of two stages: extracting features on face images and training a classifier based on the extracted features. Instead, we tackle the problem via using Deep Convolution Neural Networks to extract features and classify them simultaneously. The proposed method is evaluated in three scenarios: (i) the classification of black and white people, (ii) the classification of Chinese and Non-Chinese people, and (iii) the classification of Han, Uyghurs and Non-Chinese. Experimental results on both public and self-collected databases demonstrate the effectiveness of the proposed method.

Keywords

Face Image Local Binary Pattern Chinese People Ethnicity Classification Deep Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61202161) and the National Key Scientific Instrument and Equipment Development Projects of China (No. 2013YQ49087904).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.National Key Laboratory of Fundamental Science on Synthetic Vision, School of Computer ScienceSichuan UniversityChengduChina

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