The Research of Chinese Ethnical Face Recognition Based on Deep Learning

  • Qike ZhaoEmail author
  • Tangming Chen
  • Xiaosu Zhu
  • Jingkuan Song
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11809)


Face recognition emerged in the seventies. With the introduction of deep learning methods, especially the convolution neural networks (CNNs), more and more traditional machine learning techniques have been recently superseded by them. In a multi-ethnic country like China, the study for Chinese ethnical face recognition (CEFR) has practical demands and applications. In this paper, we provide a brief of popular face recognition procedure based on deep learning method firstly. Then, as lacking of the corresponding dataset, we construct a collection of Chinese ethnical face images (CCEFI) including Han, Uygur, Tibetan and Mongolian. Based on multi-task cascaded convolution networks (MTCNN) [14] and residual networks (ResNets) [11, 12], our proposed model can achieve promising results for face detection and classification. Specifically, the average precision reaches 75% on CCEFI self-draft. Experimental results indicate that our model is able to detect the face in some constrained environments and distinguish its ethnical category. Meanwhile, the dataset established by us would be a useful dataset for relevant work.


Chinese ethnical face recognition Convolutional neural network Residual network Face detection 



This work is supported by Major Scientific and Technological Special Project of Guizhou Province (20183002).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Qike Zhao
    • 1
    • 2
    Email author
  • Tangming Chen
    • 2
  • Xiaosu Zhu
    • 2
  • Jingkuan Song
    • 1
    • 2
  1. 1.Guizhou Provincial Key Laboratory of Public Big DataGuiZhou UniversityGuiyangChina
  2. 2.Center for Future MediaUniversity of Electronic Science and Technology of ChinaChengduChina

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