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)


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 
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.



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).


  1. 1.
    Hosoi, S., Takikawa, E, Kawade, M.: Ethnicity estimation with facial images. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 195–200. IEEE Computer Society (2004)Google Scholar
  2. 2.
    Lu, X., Jain, A.K.: Ethnicity identification from face images. In: Proceedings of SPIE - The International Society for Optical Engineering, vol. 5404, pp. 114–123 (2004)Google Scholar
  3. 3.
    Yang, Z., Ai, H.: Demographic classification with local binary patterns. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 464–473. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Toderici, G., O’Malley, S.M., Passalis, G., Theoharis, T., Kakadiaris, I.A.: Ethnicity- and gender-based subject retrieval using 3-D face-recognition techniques. Int. J. Comput. Vis. 89(2–3), 382–391 (2010)CrossRefGoogle Scholar
  5. 5.
    Guo, G., Mu, G.: A study of large-scale ethnicity estimation with gender and age variations. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 79–86 (2010)Google Scholar
  6. 6.
    Huang, D., Ding, H., Wang, C., Wang, Y., Zhang, G., Chen, L.: Local circular patterns for multi-modal facial gender and ethnicity classification. Image Vis. Comput. 32(12), 1181–1193 (2014)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A.: Convolutional Deep Belief Networks on CIFAR-10 (2012)Google Scholar
  8. 8.
    Ricanek, K., Tesafaye, T.: MORPH: a longitudinal image database of normal adult age-progression. In: IEEE Conference on AFGR, pp. 341–345 (2006)Google Scholar
  9. 9.
    Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. arXiv preprint arXiv, pp. 341–345 (2014)Google Scholar
  10. 10.
    Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL large-scale chinese face database and baseline evaluations. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 38(1), 149–161 (2008)CrossRefGoogle Scholar
  11. 11.
    Phillips, P.J., Moon, H., Rizvi, S., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Miao, Z., Wu, Q.M.J., Wan, Y., Tang, Z.: Low-resolution face recognition: a review. Vis. Comput. 30(4), 359–386 (2014)CrossRefGoogle Scholar
  13. 13.
    Lyle, J.R.: Soft biometric classification using periocular region features. In: Fourth IEEE International Conference on Biometrics: Theory Applications and Systems, IEEE (2010)Google Scholar
  14. 14.
    Xie, Y., Luu, K., Savvides, M.: A robust approach to facial ethnicity classification on large scale face databases. In: IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems, pp. 143–149 (2012)Google Scholar
  15. 15.
    Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Computer Science, pp. 3730–3738 (2014)Google Scholar
  16. 16.
    Zhong, Y., Sullivan, J., Li, H.: Face attribute prediction with classification CNN. In: Computer Science (2016)Google Scholar
  17. 17.
    KYan, Z., Jagadeesh, V., Decoste, D., Di, W., Piramuthu, R.: HD-CNN: Hierarchical Deep convolutional neural network for image classification. Eprint Arxiv (2014)Google Scholar
  18. 18.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet Classification with Deep Convolutional Neural Networks. In: Advances in Neural Information Processing Systems, vol. 25(2) (2012)Google Scholar
  19. 19.
    Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M., Tubaro, S.: Deep convolutional neural networks for pedestrian detection. In: Computer Science (2015)Google Scholar
  20. 20.
    Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5325–5334 (2015)Google Scholar
  21. 21.
    Ranjan, R., Patel, V.M., Chellappa, R.: Hyperface: a deep multi-task learning framework for face detection, landmark localization, pose estimation, and gender recognition. In: Computer Vision and Pattern Recognition (2016)Google Scholar
  22. 22.
    Sun, Y., Wang, X., Tang, X.: Sparsifying neural network connections for face recognition. In: Computer Science (2015)Google Scholar
  23. 23.
    Masi, L., Rawls, S., Medioni, G., Natarajan, P.: Pose-aware face recognition in the wild. In: Computer Vision and Pattern Recognition (2016)Google Scholar
  24. 24.
    Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 1–8 (2008)Google Scholar

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

Personalised recommendations