Download PDF
Facial Plast Surg 2015; 31(05): 431-438
DOI: 10.1055/s-0035-1564720
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Fast and Accurate Digital Morphometry of Facial Expressions

Carl Martin Grewe
1   Therapy Planning Group, Mathematics for Life and Material Sciences Zuse Institute Berlin, Berlin, Germany
,
Lisa Schreiber
2   Visual Knowledge, Center for Literary and Cultural Research, Berlin, Germany
,
Stefan Zachow
1   Therapy Planning Group, Mathematics for Life and Material Sciences Zuse Institute Berlin, Berlin, Germany
› Author Affiliations
Further Information

Publication History

Publication Date:
18 November 2015 (online)

    Permissions and Reprints

Abstract

Facial surgery deals with a part of the human body that is of particular importance in everyday social interactions. The perception of a person's natural, emotional, and social appearance is significantly influenced by one's expression. This is why facial dynamics has been increasingly studied by both artists and scholars since the mid-Renaissance. Currently, facial dynamics and their importance in the perception of a patient's identity play a fundamental role in planning facial surgery. Assistance is needed for patient information and communication, and documentation and evaluation of the treatment as well as during the surgical procedure. Here, the quantitative assessment of morphological features has been facilitated by the emergence of diverse digital imaging modalities in the last decades. Unfortunately, the manual data preparation usually needed for further quantitative analysis of the digitized head models (surface registration, landmark annotation) is time-consuming, and thus inhibits its use for treatment planning and communication. In this article, we refer to historical studies on facial dynamics, briefly present related work from the field of facial surgery, and draw implications for further developments in this context. A prototypical stereophotogrammetric system for high-quality assessment of patient-specific 3D dynamic morphology is described. An individual statistical model of several facial expressions is computed, and possibilities to address a broad range of clinical questions in facial surgery are demonstrated.

Keywords

facial expressions - facial dynamics - computer-aided planning - statistical shape model - stereophotogrammetry
 

  • References

  • 1 Le Brun C La méthode pour apprendre à dessiner les passions. Reprint: Hildesheim: Georg Olms Verlag; 1702. Available at: https://archive.org/details/methodepourappre00lebr . Accessed August 13, 2015
    PubMed
  • 2 Schmidt A. Gefühle zeigen, Gefühle deuten. In: Frevert U, Scheer M, Schmidt A, , et al. eds. Gefühlswissen. Frankfurt: Campus Verlag; 2011: 65-100
    Google Scholar
  • 3 Duchenne GB. Mécanisme de la physionomie humaine ou analyse électro-physiologique de l'expression des passion. Paris: Jules Renouard; 1862. . Available at: https://archive.org/details/Duchenne1862oj91W . Accessed August 13, 2015
    CrossrefGoogle Scholar
  • 4 Weigel S. Grammatologie der Bilder. Berlin: Suhrkamp; 2015
    Google Scholar
  • 5 Ekman P, Hager JC, Friesen WV. Facial Action Coding System. The Manual on CD Rom. Salt Lake City, UT: Network Information Research Corporation; 2002
    Google Scholar
  • 6 Ramsbrock A. Korrigierte Körper. Eine Geschichte künstlicher Schönheit in der Moderne. Göttingen: Wallstein Verlag; 2011
    Google Scholar
  • 7 Wodak E. Nasen-, Ohren- und Gesichtsplastik: Ästhetische und psychologische Grundlagen. Berlin: Urban & Schwarzenberg; 1938
    Google Scholar
  • 8 Inthorn J. Wie schön wollen wir sein? Normative Selbstvergewisserungsprozesse in medialen Debatten zur Schönheitschirurgie. In: Borkenhagen A, Brinkschulte E, Brähler E, , eds. Homo plasticus - Psychosoziale Aspekte schönheitschirurgischen Enhancements. Gießen: Psychosozial-Verlag; 2013: 13-21
    Google Scholar
  • 9 Williams S. The 3-D Approach to Cosmetic Dermatology. J Clin Dermatol 2010; 1 (2) 59-66
    PubMedGoogle Scholar
  • 10 Gassia V, Raspaldo H, Niforos FR, Michaud T. Global 3-dimensional approach to natural rejuvenation: recommendations for perioral, nose, and ear rejuvenation. J Cosmet Dermatol 2013; 12 (2) 123-136
    CrossrefPubMedGoogle Scholar
  • 11 Scharschmidt D. Botulinumtoxin und Filler. Der Trend zu minimalinvasiven Eingriffen. In: Borkenhagen A, Brinkschulte E, Brähler E, , eds. Homo plasticus - Psychosoziale Aspekte schönheitschirurgischen Enhancements. Gießen: Psychosozial-Verlag; 2013: 61-67
    Google Scholar
  • 12 Michaud T, Gassia V, Belhaouari L. Facial dynamics and emotional expressions in facial aging treatments. J Cosmet Dermatol 2015; 14 (1) 9-21
    CrossrefPubMedGoogle Scholar
  • 13 Gladilin E, Zachow S, Deuflhard P, Hege HC. Anatomy- and physics-based facial animation for craniofacial surgery simulations. Med Biol Eng Comput 2004; 42 (2) 167-170
    CrossrefPubMedGoogle Scholar
  • 14 Beldie L, Walker B, Lu Y, Richmond S, Middleton J. Finite element modelling of maxillofacial surgery and facial expressions–a preliminary study. Int J Med Robot 2010; 6 (4) 422-430
    CrossrefPubMedGoogle Scholar
  • 15 Nicolai JP, van de Graaf R. Facial paralysis: development of treatments in the past century. In: Beurskens C, van Gelder R, Heymans P, Manni J, Nicolai JP, , eds. The Facial Palsies. Utrecht: Lemma Publishers; 2005: 151-162
    Google Scholar
  • 16 Frey M, Michaelidou M, Tzou CH , et al. Three-dimensional video analysis of the paralyzed face reanimated by cross-face nerve grafting and free gracilis muscle transplantation: quantification of the functional outcome. Plast Reconstr Surg 2008; 122 (6) 1709-1722
    CrossrefPubMedGoogle Scholar
  • 17 Tzou CH, Pona I, Placheta E , et al. Evolution of the 3-dimensional video system for facial motion analysis: ten years' experiences and recent developments. Ann Plast Surg 2012; 69 (2) 173-185
    CrossrefPubMedGoogle Scholar
  • 18 Sawyer AR, See M, Nduka C. Quantitative analysis of normal smile with 3D stereophotogrammetry–an aid to facial reanimation. J Plast Reconstr Aesthet Surg 2010; 63 (1) 65-72
    CrossrefPubMedGoogle Scholar
  • 19 Rubin LR. The anatomy of a smile: its importance in the treatment of facial paralysis. Plast Reconstr Surg 1974; 53 (4) 384-387
    CrossrefPubMedGoogle Scholar
  • 20 Neely JG, Wang KX, Shapland CA, Sehizadeh A, Wang A. Computerized objective measurement of facial motion: normal variation and test-retest reliability. Otol Neurotol 2010; 31 (9) 1488-1492
    PubMedGoogle Scholar
  • 21 Tzou CH, Artner NM, Pona I , et al. Comparison of three-dimensional surface-imaging systems. J Plast Reconstr Aesthet Surg 2014; 67 (4) 489-497
    CrossrefPubMedGoogle Scholar
  • 22 Besl P, McKay N. Method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 1992; 14 (2) 239-256
    CrossrefPubMedGoogle Scholar
  • 23 Maal TJ, Verhamme LM, van Loon B , et al. Variation of the face in rest using 3D stereophotogrammetry. Int J Oral Maxillofac Surg 2011; 40 (11) 1252-1257
    CrossrefPubMedGoogle Scholar
  • 24 Eidson L, Cevidanes LHS, de Paula LK, Hershey HG, Welch G, Rossouw PE. Three-dimensional evaluation of changes in lip position from before to after orthodontic appliance removal. Am J Orthod Dentofacial Orthop 2012; 142 (3) 410-418
    CrossrefPubMedGoogle Scholar
  • 25 Milborrow S, Nicolls F. Active shape models with SIFT descriptors and MARS. . In: Proceedings from the International Conference on Computer Vision Theory and Applications (VISAPP), Lisbon, Portugal; 2014:5
    PubMedGoogle Scholar
  • 26 Bradley D, Heidrich W, Popa T, Sheffer A. High resolution passive facial performance capture. ACM Trans Graph 2010; 29 (4) 41-45
    PubMedGoogle Scholar
  • 27 Beeler T, Hahn F, Bradley D , et al. High-quality passive facial performance capture using anchor frames. ACM Trans Graph 2011; 30 (4) 75-85
    PubMedGoogle Scholar