Skip to main content
SpringerLink
Log in

Automatic Detection of Facial Landmarks for Denture Models

  • Conference paper
  • First Online:
Data Management Technologies and Applications (DATA 2022, DATA 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1860))

  • 159 Accesses

Abstract

The neoclassical canon proportions for face evaluation were defined by artists and anatomists in the 17th and 18th centuries. These proportions are used as a reference for planning facial or dental reconstruction treatments. However, the vertical canon assumption that the face is divided vertically into three equal thirds, which was adopted a long time ago, has not been verified yet. We used 2D photos freely available online and annotated them with anthropometric landmarks using machine learning to verify this hypothesis. Our results indicate that the vertical dimensions of the face are not always divided into equal thirds. Thus, this vertical canon should be used with caution in cosmetic, plastic, or dental surgeries, and reconstruction procedures. In addition, when working with 2D images, we observed that landmarking 2D images can be inaccurate due to pose sensitivity. To address this problem we proposed the use of 3D face landmarking. Our results indicate that regardless of the 3D face scan pose, we were able to annotate the face scans with close to accurate landmarks.

The first two authors, Ashwinee Mehta and Richard Bi, contributed equally to this article.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Agbolade, O., Nazri, A., Yaakob, R., Ghani, A.A., Kqueen Cheah, Y.: Homologous multi-points warping: an algorithm for automatic 3D facial landmark. In: 2019 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), pp. 79–84 (2019). https://doi.org/10.1109/I2CACIS.2019.8825072

  2. Al-Sebaei, M.O.: The validity of three neo-classical facial canons in young adults originating from the Arabian peninsula. Head Face Med. 11(1), 1–7 (2015)

    Article  Google Scholar 

  3. Amirkhanov, A., et al.: Visual analytics in dental aesthetics. In: Computer Graphics Forum, vol. 39, pp. 635–646. Wiley Online Library (2020)

    Google Scholar 

  4. Bozkir, M., Karakas, P., Oguz, Ö.: Vertical and horizontal neoclassical facial canons in Turkish young adults. Surg. Radiol. Anat. 26(3), 212–219 (2004)

    Article  Google Scholar 

  5. Burusapat, C., Lekdaeng, P.: What is the most beautiful facial proportion in the 21st century? Comparative study among miss universe, miss universe Thailand, neoclassical canons, and facial golden ratios. Plast. Reconstr. Surg. Glob. Open 7(2) (2019)

    Google Scholar 

  6. Cheng, S., Zafeiriou, S., Asthana, A., Pantic, M.: 3D facial geometric features for constrained local model. In: 2014 IEEE International Conference on Image Processing (ICIP) (2014). https://doi.org/10.1109/icip.2014.7025285

  7. Eboh, D.E.O.: Horizontal facial thirds of young adults in two south-south Nigerian ethnic groups. Anat. Biol. Anthropol. 32(4), 115–119 (2019)

    Article  Google Scholar 

  8. El Rai, M.C., Tortorici, C., Al-Muhairi, H., Al Safar, H., Werghi, N.: Landmarks detection on 3D face scans using local histogram descriptors. In: 2016 18th Mediterranean Electrotechnical Conference (MELECON), pp. 1–5 (2016). https://doi.org/10.1109/MELCON.2016.7495382

  9. Farkas, L.G., Hreczko, T.A., Kolar, J.C., Munro, I.R.: Vertical and horizontal proportions of the face in young adult north American Caucasians: revision of neoclassical canons. Plast. Reconstr. Surg. 75(3), 328–338 (1985)

    Article  Google Scholar 

  10. Galvánek, M., Furmanová, K., Chalás, I., Sochor, J.: Automated facial landmark detection, comparison and visualization. In: Proceedings of the 31st Spring Conference on Computer Graphics, SCCG 2015, pp. 7–14. Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2788539.2788540

  11. Gilani, S.Z., Shafait, F., Mian, A.: Shape-based automatic detection of a large number of 3D facial landmarks. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015). https://doi.org/10.1109/cvpr.2015.7299095

  12. Guo, J., Zhu, X., Lei, Z.: 3DDFA (2018). https://github.com/cleardusk/3DDFA

  13. Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., Li, S.Z.: Towards fast, accurate and stable 3D dense face alignment. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)

    Google Scholar 

  14. Gupta, S., Castleman, K.R., Markey, M.K., Bovik, A.C.: Texas 3D face recognition database. In: 2010 IEEE Southwest Symposium on Image Analysis & Interpretation (SSIAI), pp. 97–100. IEEE (2010)

    Google Scholar 

  15. Gupta, S., Markey, M.K., Bovik, A.C.: Anthropometric 3D face recognition. Int. J. Comput. Vision 90(3), 331–349 (2010)

    Article  Google Scholar 

  16. Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, 07-49, University of Massachusetts, Amherst (2007)

    Google Scholar 

  17. de Jong, M.A., et al.: An automatic 3D facial landmarking algorithm using 2D Gabor wavelets. IEEE Trans. Image Process. 25(2), 580–588 (2016). https://doi.org/10.1109/TIP.2015.2496183

    Article  MathSciNet  MATH  Google Scholar 

  18. King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)

    Google Scholar 

  19. Koenderink, J.J., van Doorn, A.J.: Surface shape and curvature scales. Image Vis. Comput. 10, 557–564 (1992)

    Article  Google Scholar 

  20. Le, T.T., Farkas, L.G., Ngim, R.C., Levin, L.S., Forrest, C.R.: Proportionality in Asian and North American Caucasian faces using neoclassical facial canons as criteria. Aesthetic Plast. Surg. 26(1), 64–69 (2002)

    Article  Google Scholar 

  21. Liu, J., Zhang, Q., Tang, C.: Automatic landmark detection for high resolution non-rigid 3D faces based on geometric information. In: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (2015). https://doi.org/10.1109/iaeac.2015.7428562

  22. Marcolin, F., Vezzetti, E.: Novel descriptors for geometrical 3D face analysis. Multimedia Tools Appl. 76(12), 13805–13834 (2016). https://doi.org/10.1007/s11042-016-3741-3

    Article  Google Scholar 

  23. Mehta, A., Abdelaal, M., Sheba, M., Herndon, N.: Automated neoclassical vertical canon validation in human faces with machine learning. In: Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, pp. 461–467. INSTICC, SciTePress (2022). https://doi.org/10.5220/0011300200003269

  24. Milborrow, S., Morkel, J., Nicolls, F.: The MUCT Landmarked Face Database. Pattern Recognition Association of South Africa (2010). http://www.milbo.org/muct

  25. Mutsvangwa, T., et al.: Design, construction, and testing of a stereo-photogrammetric tool for the diagnosis of fetal alcohol syndrome in infants. IEEE Trans. Med. Imaging 28(9), 1448–1458 (2009). https://doi.org/10.1109/tmi.2009.2017375

    Article  Google Scholar 

  26. Patrikalakis, N.M., Maekawa, T.: Shape interrogation for computer aided design and manufacturing (2010). https://doi.org/10.1007/978-3-642-04074-0

  27. Paulsen, R.R., Juhl, K.A., Haspang, T.M., Hansen, T.F., Ganz, M., Einarsson, G.: Multi-view consensus CNN for 3D facial landmark placement. CoRR abs/1910.06007 (2019). http://arxiv.org/abs/1910.06007

  28. Pavlic, A., Zrinski, M.T., Katic, V., Spalj, S.: Neoclassical canons of facial beauty: do we see the deviations? J. Cranio-Maxillofac. Surg. 45(5), 741–747 (2017)

    Article  Google Scholar 

  29. Schmid, K., Marx, D., Samal, A.: Computation of a face attractiveness index based on neoclassical canons, symmetry, and golden ratios. Pattern Recogn. 41(8), 2710–2717 (2008)

    Article  Google Scholar 

  30. Sullivan, C.B., Kaszynski, A.: PyVista: 3D plotting and mesh analysis through a streamlined interface for the visualization toolkit (VTK). J. Open Source Softw. 4(37), 1450 (2019). https://doi.org/10.21105/joss.01450

  31. Sun, J., Huang, D., Wang, Y., Chen, L.: Expression robust 3D facial landmarking via progressive coarse-to-fine tuning. ACM Trans. Multimed. Comput. Commun. Appl. 15(1), 1–23 (2019). https://doi.org/10.1145/3282833

    Article  Google Scholar 

  32. Terada, T., Chen, Y.W., Kimura, R.: 3D facial landmark detection using deep convolutional neural networks. In: 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) (2018). https://doi.org/10.1109/fskd.2018.8687254

  33. Vezzetti, E., Marcolin, F.: Geometry-based 3D face morphology analysis: soft-tissue landmark formalization. Multimedia Tools Appl. 68(3), 895–929 (2012). https://doi.org/10.1007/s11042-012-1091-3

    Article  Google Scholar 

  34. Vezzetti, E., Marcolin, F.: 3D landmarking in multiexpression face analysis: a preliminary study on eyebrows and mouth. Aesthetic Plast. Surg. 38(4), 796–811 (2014). https://doi.org/10.1007/s00266-014-0334-2

    Article  Google Scholar 

  35. Wang, X., Tang, X.: Face photo-sketch synthesis and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 31(11), 1955–1967 (2009). https://doi.org/10.1109/TPAMI.2008.222

    Article  Google Scholar 

  36. Zhang, J., Gao, K., Zhao, Q., Wang, D.: Pose invariant 3D facial landmark detection via pose normalization and deep regression. In: 2020 2nd International Conference on Image Processing and Machine Vision (2020). https://doi.org/10.1145/3421558.3421570

Download references

Acknowledgements

This work was supported in part by NSF REU grant #2050883.

Author information

Authors and Affiliations

  1. Department of Computer Science, East Carolina University, Greenville, NC, USA

    Ashwinee Mehta & Nic Herndon

  2. School of Dental Medicine, East Carolina University, Greenville, NC, USA

    Sheba Moamen & Maged Abdelaal

  3. Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Champaign, IL, USA

    Richard Bi

Authors
  1. Ashwinee Mehta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Richard Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sheba Moamen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Maged Abdelaal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nic Herndon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nic Herndon .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Alfredo Cuzzocrea

  2. Ford Motor Company, Commerce Township, MI, USA

    Oleg Gusikhin

  3. Siège du Groupe ESEO, Angers, France

    Slimane Hammoudi

  4. Hochschule Niederrhein, Krefeld, Nordrhein-Westfalen, Germany

    Christoph Quix

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mehta, A., Bi, R., Moamen, S., Abdelaal, M., Herndon, N. (2023). Automatic Detection of Facial Landmarks for Denture Models. In: Cuzzocrea, A., Gusikhin, O., Hammoudi, S., Quix, C. (eds) Data Management Technologies and Applications. DATA DATA 2022 2021. Communications in Computer and Information Science, vol 1860. Springer, Cham. https://doi.org/10.1007/978-3-031-37890-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37890-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37889-8

  • Online ISBN: 978-3-031-37890-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation