Multi-ethnical Chinese facial characterization and analysis

22 January 2021 Editor’s Note: Concerns have been raised about the ethics approval and informed consent procedures related to the research reported in this paper. Editorial action will be taken as appropriate once an investigation of the concerns is complete and all parties have been given an opportunity to respond in full.

Abstract

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.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Change history

  • 22 January 2021

    Editor’s Note: Concerns have been raised about the ethics approval and informed consent procedures related to the research reported in this paper. Editorial action will be taken as appropriate once an investigation of the concerns is complete and all parties have been given an opportunity to respond in full.

References

  1. 1.

    Balasubramanian M, Schwartz EL (2002) The isomap algorithm and topological stability

  2. 2.

    Ball R (2011) SizeChina: a 3D anthropometric survey of the Chinese head. TU Delft, Delft University of Technology

  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 2015

  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):244–252

    Article  Google 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):874

    Article  Google Scholar 

  6. 6.

    Das S (2001) Filters, wrappers and a boosting-based hybrid for feature selection. In: ICML, vol 1, pp 74–81

  7. 7.

    Enlow DH, Moyers RE (1982) Handbook of facial growth. Saunders, Philadelphia

    Google Scholar 

  8. 8.

    Farkas LG (1994) Anthropometry of the head and face. Raven Pr

  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):615–646

    Article  Google 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 2012:19

    Article  Google 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):2483–2509

    Article  Google 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):149–161

    Article  Google 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 97–100

  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 194–199

  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):948–960

    Article  Google 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):1502–1516

    Article  Google 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):328–340

    Article  Google 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 13/059,424

  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):123–142

    Article  Google 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):297–303

    Google 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):404–408

    Google 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 9988–9991

  23. 23.

    Lu X, Chen H, Jain AK (2006) Multimodal facial gender and ethnicity identification. In: International conference on biometrics. Springer, pp 554–561

  24. 24.

    Milborrow S Active shape models with stasm, Stasm Version 3

  25. 25.

    Ou Y, Wu X, Qian H, Xu Y (2005) A real time race classification system, IEEE

  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):1226–1238

    Article  Google 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):295–306

    Article  Google 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 411–418

  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–405

  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 380–386

  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):1146

    Article  Google Scholar 

  32. 32.

    Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75

    Article  Google 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):565–574

    Article  Google 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):24457–24475

    Article  Google 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):1921–1930

    Google 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):221–227

    Article  Google 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):e41193

    Article  Google 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):63–76

    Article  Google 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):381–391

    Google 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 82–90

  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 64:22–31

    Article  Google Scholar 

  42. 42.

    Zhao S, Yao H, Sun X (2013) Video classification and recommendation based on affective analysis of viewers. Neurocomputing 119:101–110

    Article  Google 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):632–645

    Article  Google Scholar 

  44. 44.

    Zhuang Z, Bradtmiller B (2005) Head-and-face anthropometric survey of us respirator users. J Occup Environ Hyg 2(11):567–576

    Article  Google 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. meq007

Download references

Acknowledgements

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).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiaodong Duan.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, C., Zhang, Q., Duan, X. et al. Multi-ethnical Chinese facial characterization and analysis. Multimed Tools Appl 77, 30311–30329 (2018). https://doi.org/10.1007/s11042-018-6018-1

Download citation

Keywords

  • Chinese ethnicity
  • Manifold learning
  • Ethnicity classification