Multimedia Tools and Applications

, Volume 77, Issue 23, pp 30311–30329 | Cite as

Multi-ethnical Chinese facial characterization and analysis

  • Cunrui Wang
  • Qingling Zhang
  • Xiaodong DuanEmail author
  • Jianhou Gan


Facial image based characterization and analysis of ethnicity, which is an important index of human demography, have become increasingly popular in the research areas of pattern recognition, computer vision, and machine learning. Many applications, such as face recognition and facial expression recognition, are affected by ethnicity information of individuals. In this study, we first create a human face database, which focuses on human ethnicity information and includes individuals from eight ethnic groups in China. This dataset can be used to conduct psychological experiments or evaluate the performance of computational algorithms. To evaluate the usefulness of this created dataset, some critical landmarks of these face images are detected and three types of features are extracted as ethnicity representations. Next, the ethnicity manifolds are learnt to demonstrate the discriminative power of the extracted features. Finally, ethnicity classifications with different popular classifiers are conducted on the constructed database, and the results indicate the effectiveness of the proposed features.


Chinese ethnicity Manifold learning Ethnicity classification 



This work is sponsored by Natural Science Foundation of China (61370146, 61672132), National Science and Technology Support Program (2013BAJ07B02) and Science & Technology Project of Liaoning Province (No.2013405003).


  1. 1.
    Balasubramanian M, Schwartz EL (2002) The isomap algorithm and topological stabilityCrossRefGoogle Scholar
  2. 2.
    Ball R (2011) SizeChina: a 3D anthropometric survey of the Chinese head. TU Delft, Delft University of TechnologyGoogle Scholar
  3. 3.
    Benlamoudi A, Samai D, Ouafi A, Taleb-Ahmed A, Bekhouche SE, Hadid A (2015) Face spoofing detection from single images using active shape models with stasm and lbp. In: Proceeding of the Troisime conference internationale sur la vision artificielle CVA 2015Google Scholar
  4. 4.
    Choe KS, Sclafani AP, Litner JA, Yu G-P, Romo T (2004) The korean american woman’s face: anthropometric measurements and quantitative analysis of facial aesthetics. Arch Facial Plast Surg 6(4):244–252CrossRefGoogle Scholar
  5. 5.
    Dailey MN, Joyce C, Lyons MJ, Kamachi M, Ishi H, Gyoba J, Cottrell GW (2010) Evidence and a computational explanation of cultural differences in facial expression recognition. Emotion 10(6):874CrossRefGoogle Scholar
  6. 6.
    Das S (2001) Filters, wrappers and a boosting-based hybrid for feature selection. In: ICML, vol 1, pp 74–81Google Scholar
  7. 7.
    Enlow DH, Moyers RE (1982) Handbook of facial growth. Saunders, PhiladelphiaGoogle Scholar
  8. 8.
    Farkas LG (1994) Anthropometry of the head and face. Raven PrGoogle Scholar
  9. 9.
    Farkas LG, Katic MJ, Forrest CR (2005) International anthropometric study of facial morphology in various ethnic groups/races. J Craniofac Surg 16(4):615–646CrossRefGoogle Scholar
  10. 10.
    Fu S-Y, Yang G-S, Kuai X-K (2012) A spiking neural network based cortex-like mechanism and application to facial expression recognition. Comput Intell Neurosci 2012:19CrossRefGoogle Scholar
  11. 11.
    Fu S, He H, Hou Z-G (2014) Learning race from face: a survey. IEEE Trans Pattern Anal Mach Intell 36(12):2483–2509CrossRefGoogle Scholar
  12. 12.
    Gao W, Cao B, Shan S, Chen X, Zhou D, Zhang X, Zhao D (2008) The cas-peal large-scale chinese face database and baseline evaluations. IEEE Trans Syst Man Cybern Part A Syst Hum 38(1):149–161CrossRefGoogle Scholar
  13. 13.
    Gupta S, Castleman KR, Markey MK, Bovik AC (2010) Texas 3d face recognition database. In: IEEE southwest symposium on image analysis & interpretation (SSIAI), 2010, IEEE, pp 97–100Google Scholar
  14. 14.
    Gutta S, Wechsler H, Phillips PJ (1998) Gender and ethnic classification of face images. In: Third IEEE international conference on automatic face and gesture recognition, 1998. Proceedings. IEEE, pp 194–199Google Scholar
  15. 15.
    Gutta S, Huang JR, Jonathon P, Wechsler H (2000) Mixture of experts for classification of gender, ethnic origin, and pose of human faces. IEEE Trans Neural Netw 11(4):948–960CrossRefGoogle Scholar
  16. 16.
    Guyomarc’HP, Dutailly B, Charton J, Santos F, Desbarats P, Coqueugniot H (2015) Anthropological facial approximation in three dimensions (afa3d): computer-assisted estimation of the facial morphology using geometric morphometrics. J Forensic Sci 59(6):1502–1516CrossRefGoogle Scholar
  17. 17.
    He X, Yan S, Hu Y, Niyogi P, Zhang HJ (2005) Face recognition using laplacianfaces. IEEE Trans Pattern Anal Mach Intell 27(3):328–340CrossRefGoogle Scholar
  18. 18.
    Hosoi S (2010) Face authentication apparatus, person image search system, face authentication apparatus control program, computer-readable recording medium, and method of controlling face authentication apparatus, uS Patent App 13/059,424Google Scholar
  19. 19.
    Huang Y, Yao H, Zhao S, Zhang Y (2017) Towards more efficient and flexible face image deblurring using robust salient face landmark detection. Multimed Tools Appl 76(1):123–142CrossRefGoogle Scholar
  20. 20.
    Jianwen H, Lihua W, Longguang L, Shourong C (2010) Analysis of morphous characteristics of facial reconstruction and the five organs in chinese north five national minorities crowd. J Chongqing Med Univ 35(2):297–303Google Scholar
  21. 21.
    Keli Y, Lianbin Z, Yonglan L, Huanjiu X, Sunhua L (2016) Differences of head-face morphological traits between southern and northern chinese han. Acta Anat Sin 47(3):404–408Google Scholar
  22. 22.
    Lin H, Lu H, Zhang L (2006) A new automatic recognition system of gender, age and ethnicity. In: 2006 6th world congress on intelligent control and automation, vol 2. IEEE, pp 9988–9991Google Scholar
  23. 23.
    Lu X, Chen H, Jain AK (2006) Multimodal facial gender and ethnicity identification. In: International conference on biometrics. Springer, pp 554–561Google Scholar
  24. 24.
    Milborrow S Active shape models with stasm, Stasm Version 3Google Scholar
  25. 25.
    Ou Y, Wu X, Qian H, Xu Y (2005) A real time race classification system, IEEEGoogle Scholar
  26. 26.
    Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238CrossRefGoogle Scholar
  27. 27.
    Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image Vis Comput 16(5):295–306CrossRefGoogle Scholar
  28. 28.
    Qiu X, Sun Z, Tan T (2006) Global texture analysis of iris images for ethnic classification. In: International conference on biometrics. Springer, pp 411–418Google Scholar
  29. 29.
    Qiu X, Sun Z, Tan T (2007) Learning appearance primitives of iris images for ethnic classification. In: 2007 IEEE international conference on image processing, vol 2. IEEE, pp II–405Google Scholar
  30. 30.
    Robinette KM, Daanen H, Paquet E (1999) The caesar project: a 3-d surface anthropometry survey. In: Second international conference on 3-D digital imaging and modeling, 1999. Proceedings. IEEE, pp 380–386Google Scholar
  31. 31.
    Sauter SL, Murphy LR, Hurrell JJ (1990) Prevention of work-related psychological disorders: a national strategy proposed by the national institute for occupational safety and health (niosh). Am Psychol 45(10):1146CrossRefGoogle Scholar
  32. 32.
    Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75CrossRefGoogle Scholar
  33. 33.
    Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl 28(3):565–574CrossRefGoogle Scholar
  34. 34.
    Semwal VB, Singha J, Sharma PK, Chauhan A, Behera B (2017) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl 76(22):24457–24475CrossRefGoogle Scholar
  35. 35.
    Siffert W, Forster P, Jöckel K-H, Mvere DA, Brinkmann B, Naber C, Crookes R, HEYNS ADP, EPPLEN JT, Fridey J et al (1999) Worldwide ethnic distribution of the g protein \(\beta \)3 subunit 825t allele and its association with obesity in caucasian, chinese, and black african individuals. J Am Soc Nephrol 10(9):1921–1930Google Scholar
  36. 36.
    Snow CC, Gatliff BP, Mcwilliams KR (2010) Reconstruction of facial features from the skull: an evaluation of its usefulness in forensic anthropology. Am J Phys Anthropol 33(2):221–227CrossRefGoogle Scholar
  37. 37.
    Strom MA, Zebrowitz LA, Zhang S, Bronstad PM, Lee HK (2012) Skin and bones: the contribution of skin tone and facial structure to racial prototypicality ratings. PloS One 7(7):e41193CrossRefGoogle Scholar
  38. 38.
    Yao Y-G, Nie L, Harpending H, Fu Y-X, Yuan Z-G, Zhang Y-P (2002) Genetic relationship of chinese ethnic populations revealed by mtdna sequence diversity. Am J Phys Anthropol 118(1):63–76CrossRefGoogle Scholar
  39. 39.
    Zhang H, Ding M, Jiao Y, Wang X, Yan Z, Jin G, Meng X, Bai C, Lu Z, Chen R (1997) A dermatoglyphic study of the chinese population dermatoglyphics cluster of fifty-two nationalities in China. Acta Genet Sin 25(5):381–391Google Scholar
  40. 40.
    Zhang H, Sun Z, Tan T, Wang J (2011) Ethnic classification based on iris images. In: Chinese conference on biometric recognition, Springer, pp 82–90Google Scholar
  41. 41.
    Zhang Y, Wang S, Phillips P, Ji G (2014) Binary pso with mutation operator for feature selection using decision tree applied to spam detection. Knowl-Based Syst 64:22–31CrossRefGoogle Scholar
  42. 42.
    Zhao S, Yao H, Sun X (2013) Video classification and recommendation based on affective analysis of viewers. Neurocomputing 119:101–110CrossRefGoogle Scholar
  43. 43.
    Zhao S, Yao H, Gao Y, Ji R, Ding G (2017) Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans Multimed 19(3):632–645CrossRefGoogle Scholar
  44. 44.
    Zhuang Z, Bradtmiller B (2005) Head-and-face anthropometric survey of us respirator users. J Occup Environ Hyg 2(11):567–576CrossRefGoogle Scholar
  45. 45.
    Zhuang Z, Landsittel D, Benson S, Roberge R, Shaffer R (2010) Facial anthropometric differences among gender, ethnicity, and age groups. Ann Occup Hyg. meq007Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Cunrui Wang
    • 1
    • 2
  • Qingling Zhang
    • 1
  • Xiaodong Duan
    • 2
    Email author
  • Jianhou Gan
    • 3
  1. 1.Institute of System ScienceNortheastern UniversityShenyangChina
  2. 2.Dalian Key Lab of Digital Technology for National CultureDalian Nationalities UniversityDalianChina
  3. 3.Key Laboratory of Educational Informatization for NationalitiesYunnan Normal UniversityKunmingChina

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