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Facial Ethnicity Classification with Deep Convolutional Neural Networks

Part of the Lecture Notes in Computer Science book series (LNIP,volume 9967)


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.


  • Face Image
  • Local Binary Pattern
  • Chinese People
  • Ethnicity Classification
  • Deep Convolutional Neural Network

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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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Correspondence to Qijun Zhao .

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Wang, W., He, F., Zhao, Q. (2016). Facial Ethnicity Classification with Deep Convolutional Neural Networks. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham.

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