APPLICATION OF DIGITAL FACIAL ANTHROPOMETRY (Review paper)

Kukharev G.A., Kaziyeva N. Application of digital facial anthropometry. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2019, vol. 19, no. 2,  pp. 255–270 (in Russian). doi: 10.17586/2226-1494-2019-19-2-255-270

An analytical review of the digital facial anthropometry application in the scientific and practical problems of the contemporary human world is presented. The research includes a brief history of anthropometry emergence (as a descriptive and comparative science, the subject of which is human) and its transformation into contemporary computer methods of digital anthropometry. We have considered the application features of digital facial anthropometry. They are: the concepts of the morphotype and phenotype of the face, problems of morphology and morphometry, as the basic means of digital facial anthropometry, methods of quantitative and qualitative assessment of the characteristics and parameters of individuals, the task of finding associations between gene sets in the genome and human facial features, the task of assessing the attractiveness and beauty of individuals, the relationship of facial anthropometry with "Chernoff Faces" and cognitive computer graphics used in practical medicine, the phenomenon of the human face and the problem of determining the emotions, sex and psycho-type of a person, special cases of face image recognition tasks, methods for solving them within the framework of digital facial anthropometry and examples of their solution. We have determined the prognosis for the close connection of digital facial anthropometry and the “Internet of things” in the contemporary world.

1. Bertilonazh - the art of identification. Available at: // kriminalisty.ru/stati/istorija-kriminalistiki/bertilyonaj.html (accessed: 10.12.2018).

2. Gerasimov M.M. Basis of Facial Reconstruction on the Skull. Moscow, Sovetskaya Nauka Publ., 1949, 190 p. (in Russian)

3. Mareev O.V., Nikolenko V.N., Aleshkina O.U. Computer craniometry with the help of modern technology in medical craniology. Morphological Newsletter, 2015, no. 1, pp. 49–54. (in Russian)

4. Jayaratne Y., Zwahlen R. Application of digital anthropometry for craniofacial assessment. Craniomaxillofacial Trauma and Reconstruction, 2014, vol. 7, no. 2, pp. 101–107. doi: 10.1055/s0034-1371540

5. Kukharev G.A., Kaziyeva N., Tsymbal D.A. Barcoding technologies for facial biometrics: current status and new solutions. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2018, vol. 18, no. 1, pp. 72–86 (in Russian). doi: 10.17586/2226-1494-2018-18-1-72-86

6. DeCarlo D., Metaxas D., Stone M. An anthropometric face model using variational techniques. Proc. 25th Annual Conf. on Computer Graphics and Interactive Techniques. New York, USA, 1998, pp. 67–74. doi: 10.1145/280814.280823

7. Deutsch C.K., Shell A.R., Francis R.W., Birs B.D. The Farkas system of craniofacial anthropometry: methodology and normative databases. In Handbook of Anthropometry. Springer, 2012, pp. 561–573. doi 10.1007/978-1-4419-1788-1_29

8. Aynechia N., Larson B.E., Leon-Salazar V., Beiraghi S. Accuracy and precision of a 3D anthropometric facial analysis with and without landmark labeling before image acquisition. Angle Orthodontist, 2011, vol. 81, no. 2, pp. 245–252. doi: 10.2319/041810-210.1

9. Stegmann M.B. Analysis and Segmentation of Face Images using Point Annotations and Linear Subspace Techniques. Technical report, 2002, 25 p. Available at: http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/922/ pdf/imm922.pdf (accessed: 02.01.2019).

10. Gupta S., Castleman K.R., Markey M.K., Bovik A.C. Texas 3D face recognition database. Proc. IEEE Southwest Symposium on Image Analysis and Interpretation. Austin, USA, 2010, pp. 97–100. doi: 10.1109/SSIAI.2010.5483908

11. Gupta S., Markey M.K., Bovik A.C. Anthropometric 3D face recognition. International Journal of Computer Vision, 2010, vol. 90, no. 3, pp. 331–349. doi: 10.1007/s11263-010-0360-8

12. CUHK Face Sketch Database. Available at: http://mmlab.ie.cuhk.edu.hk/facesketch.html (accessed 03.01.2019).

13. CUHK Face Sketch FERET Database (CUFSF). Available at: http://mmlab.ie.cuhk.edu.hk/archive/cufsf/ (accessed: 03.01.2019).

14. Wang X., Tang X. Face photo-sketch synthesis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, vol. 31, no. 11, pp. 1955–1967. doi: 10.1109/TPAMI.2008.222

15. Luxand - Face Recognition, Face Detection and Facial Feature Detection Technologies. Available at: http://www.luxand.com (accessed: 04.01.2019).

16. Software "Portret Client 5.0". System "Portrait-Search". Available at: http://www.portret.tomsk.ru/index.php?page=products (accessed: 04.01.2019).

17. Viola P., Jones M.I. Robust real-time face detection. International Journal of Computer Vision, 2004, vol. 57, no. 2, pp. 137–154. doi: 10.1023/B:VISI.0000013087.49260.fb

18. Kazemi V., Sullivan J. One millisecond face alignment with an ensemble of regression trees. Proc. 27th IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014, pp. 1867–1874. doi: 10.1109/CVPR.2014.241

19. Torres-Restrepo A.M. et al. Agreement between cranial and facial classification through clinical observation and anthropometric measurement among envigado school children. BMC Oral Health, 2014, vol. 14, no. 1, pp. 50–57. doi: 10.1186/1472-6831-14-50

20. Driessen P.J., Vuyk H., Borgstein J. New insights into facial anthropometry in digital photographs using iris dependent calibration. International Journal of Pediatric Otorhinolaryngology, 2011, vol. 75, no. 4, pp. 579–584. doi: 10.1016/j.ijporl.2011.01.023

21. Farkas L.G., Katic M.J., Forrest C.R. et al. International anthropometric study of facial morphology in various ethnic groups/races. Journal of Craniofacial Surgery, 2005, vol. 16, no. 4, pp. 615–646. doi: 10.1097/01.scs.0000171847.58031.9e

22. Ramires R.R. et al. Proposal for facial type determination based on anthropometry. Jornal Da Sociedade Brasileira De Fonoaudiologia, 2011, vol. 23, no. 3, pp. 195–200. doi: 10.1590/S2179-64912011000300003

23. Arapović-Savić M. et al. Linear measurements of facial morphology using automatic aproach. Serbian Dental Journal, 2016, vol. 63, no. 2. doi: 10.1515/sdj-2016-0007

24. Mackenzie S., Wilkinson C. Morphological and morphometric changes in the faces of female-to-male (FtM) transsexual people. International Journal of Transgenderism, 2017, vol. 18, no. 2, pp. 172–181. doi: 10.1080/15532739.2017.1279581

25. Ramanathan N., Chellappa R. Modeling age progression in young faces. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, 2006. doi: 10.1109/cvpr.2006.187

26. Sforza C., Grandi G., Menezes M. et al. Age- and sex-related changes in the normal human external nose. Forensic Science International, 2010, vol. 204, no. 1-3, pp. 205.e1–205.e9. doi: 10.1016/j.forsciint.2010.07.027

27. Kumar S., Ranjitha S., Suresh H.N. An active age estimation of facial image using anthropometric model and fast ICA. Journal of Engineering Science and Technology Review, 2017, vol. 10, no. 1, pp. 100–106. doi: 10.25103/jestr.101.14

28. Du L. et al. GARP-Face: balancing privacy protection and utility preservation in face de-identification. IEEE International Joint Conference on Biometrics. Clearwater, USA, 2014. doi: 10.1109/BTAS.2014.6996249

29. De la Torre F., Cohn J.F., Huang D. System and Method for Processing Video to Provide Facial De-Identification. Patent US 9799096 B1, 2017.

30. Lanitis A., Taylor C.J., Cootes T.F. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, vol. 19, no. 7, pp. 743–756. doi: 10.1109/34.598231

31. Cootes T.F., Edwards G.J., Taylor C.J. Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001, vol. 23, no. 6, pp. 681–685. doi: 10.1109/34.927467

32. Sucontphunt T., Neumann U. 3D facial surface and texture synthesis using 2D landmarks from a single face sketch. Proc. 2nd Int. Conf. on 3D Imaging, Modeling, Processing, Visualization and Transmission, 2012, pp. 152–159. doi: 10.1109/3DIMPVT.2012.65

33. Sforza C. et al. Three-dimensional facial morphometry: from anthropometry to digital morphology. In Handbook of Anthropometry: Physical Measures of Human Form in Health and Disease. Springer, 2012, pp. 611–624. doi: 10.1007/978-1- 4419-1788-1_32

34. Krutikova O., Glaz A. Development of a new method for adapting a 3D model from a minimum number of 2D images. Technologies of Computer Control, 2013, vol. 14, pp. 12–17.

35. Truong P.H., Park C.W., Lee M. et al. Rapid implementation of 3D facial reconstruction from a single image on an android mobile device. KSII Transactions on Internet and Information Systems, 2014, vol. 8, no. 5, pp. 1690–1710. doi: 10.3837/tiis.2014.05.011

36. Sforza C., Ferrario V.F. Soft-tissue facial anthropometry in three dimensions: from anatomical landmarks to digital morphology in research, clinics and forensic anthropology. Journal of Anthropological Sciences, 2006, vol. 84, pp. 97–124.

37. Schmid K., Marx D., Samal A. Computation of a face atractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recognition, 2008, vol. 41, no. 8, pp. 2710–2717. doi: 10.1016/j.patcog.2007.11.022

38. Pallett P.M., Link S., Lee K. New “golden” ratios for facial beauty. Vision Research, 2010, vol. 50, no. 2, pp. 149–154. doi: 10.1016/j.visres.2009.11.003

39. Soler C. et al. Male facial anthropometry and attractiveness. Perception, 2012, vol. 41, no. 10, pp. 1234–1245. doi: 10.1068/p7214

40. Milutinovic J., Zelic K., Nedeljkovic N. Evaluation of facial beauty using anthropometric proportions. The Scientific World Journal, 2014, vol. 2014. doi: 10.1155/2014/428250

41. Alam M.K., Mohd Noor N.F., Basri R., Yew T.F., Wen T.H. Multiracial facial golden ratio and evaluation of facial appearance. PLoS ONE, 2015, vol. 10, no. 11, art. e0142914 doi: 10.1371/journal.pone.0142914

42. Zhang. D., Chen F., Xu Y. Typical facial beauty analysis. In Computer Models for Facial Beauty Analysis. Springer, 2016, pp. 19–31. doi: 10.1007/978-3-319-32598-9_2

43. Prendergast P.M. Facial proportions. In Advanced Surgical Facial Rejuvenation: Art and Clinical Practice. Springer, 2012, pp. 15–22. doi: 10.1007/978-3-642-17838-2_2

44. Iskornev A. Face harmonization. Esteticheskaya Meditzina, 2017, vol. 16, no. 3, pp. 265–271. (in Russian)

45. Bagic I., Verzak Z. Craniofacial anthropometric analysis in Down's syndrome patients. Collegium Antropologicum, 2003, vol. 27, no. 2, pp. 23–30.

46. Ferrario V.F., Dellavia C., Colombo A., Sforza C. Threedimensional assessment of nose and lip morphology in subjects with Down syndrome. Annals of Plastic Surgery, 2004, vol. 53, no. 6, pp. 577–583. doi: 10.1097/01.sap.0000130702.51499.6b

47. Starbuck J., Reeves R.H., Richtsmeier J. Morphological integration of soft-tissue facial morphology in Down syndrome and siblings. American Journal of Physical Anthropology, 2011, vol. 146, no. 4, pp. 560–568. doi: 10.1002/ajpa.21583

48. Jayaratne Y.S.N. et al. The facial morphology in Down syndrome: a 3D comparison of patients with and without obstructive sleep apnea. American Journal of Medical Genetics Part A, 2017, vol. 173, no. 11, pp. 3013–3021. doi:10.1002/ajmg.a.38399

49. Yilmaz A., Akcaalan M. What can anthropometric measurements tell us about obstructive sleep apnoea? Folia Morphologica, 2017, vol. 76, no. 2, pp. 301–306. doi: 10.5603/FM.a2016.0058

50. Dering L.M. et al. Evaluation of anthropometric facial landmarks in woman with blepharophimosis, ptosis, and epicanthus inversus syndrome. RSBO, 2017, vol. 14, no. 3, pp. 147–151.

51. Axelsson J. et al. Identification of acutely sick people and facial cues of sickness. Proceedings of the Royal Society B: Biological Sciences, 2018, vol. 285, no. 1870. doi: 10.1098/rspb.2017.2430

52. Naimi A.J. et al. Investigating the relationship between major thalassemia diseases with anthropometric sizes of head and facial soft tissue. Bioscience Biotechnology Research Communications, 2017, vol. 10, no. 2, pp. 233–240. doi: 10.21786/bbrc/10.2/40

53. Farkas L.G., Katic M.J., Hreczko T.A. et al. Anthropometric proportions in the upper lip-lower lip-chin area of the lower face in young white adults. American Journal of Orthodontics, 1984, vol. 86, no. 1, pp. 52–60. doi: 10.1016/0002-9416(84)90276-8

54. Etöz A. Anthropometric analysis of the nose. In Rhinoplasty. Ed. M. Brenner. In Tech, 2011, pp. 3–10. doi: 10.5772/27218

55. Márcio F. Catapan, et al. Anthropometric analysis of human head to identification of height in proper use of ballistic helmets. Proc. 5th Int. Conf. on Applied Human Factors and Ergonomics, 2014, 12 p. 56. Goto L., et al. Analysis of a 3D anthropometric data set of children for design application. Proc. 19th Triennial Congress of the IEA. Melbourne, Australia, 2015.

57. Fenlon R. Facial respirator shape analysis using 3D anthropometric data. NIST Interagency, Internal Report, 2007, 18 p.

58. Jarkiewicz J., kocielnic R., Marasek K. Anthropometric facial emotion recognition. Lecture Notes in Computer Science, 2009, vol. 5611, pp. 188–197. doi: 10.1007/978-3-642-02577-8_21

59. Loconsole C. et al. Real-time emotion recognition: novel method for geometrical facial features extraction. Proc. Int. Conf. on Computer Vision Theory and Applications, 2014, pp. 378–385.

60. Paternoster L. et al. Genome-wide association study of threedimensional facial morphology identifies a variant in PAX3 associated with nasion position. American Journal of Human Genetics, 2012, vol. 90, no. 3, pp. 478–485. doi: 10.1016/j.ajhg.2011.12.021

61. Liu F., van der Lijn F., Schurmann C. et al. A genome-wide association study identifies five loci influencing facial morphology in Europeans. PLOS Genetics, 2012, vol. 8, no. 9. doi: 10.1371/journal.pgen.1002932

62. Claes P., Liberton D.K., Daniels K. et al. Modeling 3D facial shape from DNA. PLOS Genetics, 2014, vol. 10, no. 3. doi: 10.1371/journal.pgen.1004224

63. Shaffer J.R., Orlova E., Lee M.K. et al. Genome-wide association study reveals multiple loci influencing normal human facial morphology. PLOS Genetics, 2016. doi: 10.1371/journal.pgen.1006149

64. Lee M.K., Shaffer J.R., Leslie E.J., Orlova E., Carlson J.C., Feingold E. et al. Genome-wide association study of facial morphology reveals novel associations with FREM1 and PARK2. PLoS ONE, 2017, vol. 12, no. 4, art. e0176566. doi: 10.1371/journal.pone.0176566

65. Claes P. et al. Genome-wide mapping of global-to-local genetic effects on human facial shape. Nature Genetics, 2018, vol. 50, pp. 414–423. doi: 10.1038/s41588-018-0057-4

66. Meng C. et al. Dimension reduction techniques for the integrative analysis of multi-omics data. Briefings in Bioinformatics, 2016, vol. 17, no. 4, pp. 628–641. doi: 10.1093/bib/bbv108

67. Kukharev G.A., Shchegoleva N.L. Algorithms of twodimensional projection of digital images in Eigensubspace: history of development, implementation and application. Pattern Recognition and Image Analysis, 2018, vol. 28, no. 2, pp. 185–206. doi: 10.1134/S1054661818020116

68. Vel'kov V.V. Multidimensional biology and multidimensional medicine. Khimiya i Zhizni’, 2007, no. 3, pp. 10–15. (in Russian)

69. Chernoff H. The use of faces to represent points in K-dimensional space graphically. Journal of the American Statistical Association, 1973, vol. 68, no. 342, pp. 361–368. doi: 10.1080/01621459.1973.10482434

70. Kabulov B.T., Tashpulatova N.B. Enhanced Chernoff faces. Proc. 4th Int. Conf. on Application of Information and Communication Technologies. Tashkent, Uzbekistan, 2010. doi: 10.1109/icaict.2010.5612059

71. Osadchaya I.A., Berestneva O.G., Nemerov Ye.V. Analysis of multidimensional medical data using pictographics "Chernoff faces". Bulletin of Siberian Medicine, 2014, vol. 13, no. 4, pp. 89–93. (in Russian)

72. Kochetygov I.S., Prokop'ev R.O. Visualization of multidimensional medical data with the help of “Chernoff faces” pictographs. Proc. Int. Conf. on Information Technology in Science, Management, Social Area and Medicine. Tomsk, Russia, 2014, part 1, pp. 242–244. (in Russian)

73. Antonov A. Making Chernoff faces for data visualization. Available at: https://mathematicaforprediction.wordpress.com/2016/ 06/03/ making-chernoff-faces-for-data-visualization (accessed: 11.01.2019).

74. Panfilov S.L. Phenomenon of a Human Face in the Annex to the Hexagrams of the Book of Changes. Electronic Book, 2007, 226 p. (in Russian)

75. Yi Jing. Book of Changes. Moscow, Azbuka-Attikus, 2015, 576 p. (in Russian)

76. Krushinsky A.A. What is Yijing hexagrams? Society and State in China, 2005, vol. 35, pp. 205–213. (in Russian)

77. Ugail H., Al-dahoud A. Is gender encoded in the smile? A computational framework for the analysis of the smile driven dynamic face for gender recognition. The Visual Computer, 2018, vol. 34, no. 9, pp. 1243–1254. doi: 10.1007/s00371-018-1494-x

78. Vorob'eva Yu. Artificial intelligence has learned to distinguish between men and women by smile. Available at: www.vesti.ru/doc.html?id=2997031 (accessed: 11.01.2019).

79. Chen X., Liu C., Li B., Lu K., Song D. Targeted backdoor attacks on deep learning systems using data poisoning. arXiv:1712.05526v1, 2017.

80. Wang Y., Kosinski M. Deep neural networks can detect sexual orientation from faces. Journal of Personality and Social Psychology, 2017, vol. 114, no. 2, pp. 246–257. doi: 10.1037/pspa0000098

81. Thomas C., Kovashka A. Persuasive faces: generating faces in advertisements. Proc. British Machine Vision Conference. Tyne, UK, 2018.

82. Forczmanski P., Kukharev G., Shchegoleva N. Simple and robust facial portraits recognition under variable lighting conditions based on two-dimensional orthogonal transformations. Lecture Notes in Computer Science, 2013, vol. 8156, pp. 602–611. doi: 10.1007/978-3-642-41181-6_61

83. Kukharev G.A., Matveev Yu.N., Shchegoleva N.L. People retrieval by means of composite pictures: problem state-of-theart and technologies. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2014, no. 6, pp. 123–136. (in Russian)

84. Kukharev G., Matveev Y., Forczmanski P. An approach to improve accuracy of photo–to–sketch matching. Lecture Notes in Computer Science, 2016, vol. 9730, pp. 385–393. doi: 10.1007/978-3-319-41501-7_44

85. Gref G. Information technology sucks: the future is data economics. Available at: 2035.media/2017/10/24/greffuture/?fbclid=IwAR3fUQbQJTKGXn9D7wmC6ChELzN_bpuj H4SrIlxwbH6-t6mrHABOR1V8Ru0 (accessed: 11.01.2019).

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License