1932

Abstract

Deviation from a normal facial shape and symmetry can arise from numerous sources, including physical injury and congenital birth defects. Such abnormalities can have important aesthetic and functional consequences. Furthermore, in clinical genetics distinctive facial appearances are often associated with clinical or genetic diagnoses; the recognition of a characteristic facial appearance can substantially narrow the search space of potential diagnoses for the clinician. Unusual patterns of facial movement and expression can indicate disturbances to normal mechanical functioning or emotional affect. Computational analyses of static and moving 2D and 3D images can serve clinicians and researchers by detecting and describing facial structural, mechanical, and affective abnormalities objectively. In this review we survey traditional and emerging methods of facial analysis, including statistical shape modeling, syndrome classification, modeling clinical face phenotype spaces, and analysis of facial motion and affect.

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2022-08-10
2024-04-14
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Literature Cited

  1. 1.
    Matthews HS, Burge JA, Verhelst P-JR, Politis C, Claes PD, Penington AJ. 2020. Pitfalls and promise of 3-dimensional image comparison for craniofacial surgical assessment. Plast. Reconstr. Surg. Glob. Open 8:5e2847
    [Google Scholar]
  2. 2.
    Geng J. 2011. Structured-light 3D surface imaging: a tutorial. Adv. Opt. Photonics 3:2128–60
    [Google Scholar]
  3. 3.
    Moss JP, Linney AD, Grindrod SR, Mosse CA. 1989. A laser scanning system for the measurement of facial surface morphology. Opt. Lasers Eng. 10:3179–90
    [Google Scholar]
  4. 4.
    Weissler JM, Stern CS, Schreiber JE, Amirlak B, Tepper OM. 2017. The evolution of photography and three-dimensional imaging in plastic surgery. Plast. Reconstr. Surg. 139:3761–69
    [Google Scholar]
  5. 5.
    Petrides G, Clark JR, Low H, Lovell N, Eviston TJ. 2021. Three-dimensional scanners for soft-tissue facial assessment in clinical practice. J. Plast. Reconstr. Aesthet. Surg. 74:3605–14
    [Google Scholar]
  6. 6.
    Yoshino M, Matsuda H, Kubota S, Imaizumi K, Miyasaka S. 2000. Computer-assisted facial image identification system using a 3-D physiognomic range finder. Forensic Sci. Int. 109:3225–37
    [Google Scholar]
  7. 7.
    Velkley DE, Oliver GD Jr. 1979. Stereo-photogrammetry for the determination of patient surface geometry. Med. Phys. 6:2100–4
    [Google Scholar]
  8. 8.
    Tzou CHJ, Artner NM, Pona I, Hold A, Placheta E et al. 2014. Comparison of three-dimensional surface-imaging systems. J. Plast. Reconstr. Aesthet. Surg. 67:4489–97
    [Google Scholar]
  9. 9.
    Heike CL, Upson K, Stuhaug E, Weinberg SM. 2010. 3D digital stereophotogrammetry: a practical guide to facial image acquisition. Head Face Med 6:18
    [Google Scholar]
  10. 10.
    Rudy HL, Wake N, Yee J, Garfein ES, Tepper OM. 2020. Three-dimensional facial scanning at the fingertips of patients and surgeons: accuracy and precision testing of iPhone X three-dimensional scanner. Plast. Reconstr. Surg. 146:61407–17
    [Google Scholar]
  11. 11.
    Breitbarth A, Schardt T, Kind C, Brinkmann J, Dittrich P-G, Notni G 2019. Measurement accuracy and dependence on external influences of the iPhone X TrueDepth sensor. Photonics and Education in Measurement Science 2019, Vol. 11144 M Rosenberger, P-G Dittrich, B Zagar. https://doi.org/10.1117/12.2530544
    [Crossref] [Google Scholar]
  12. 12.
    White JD, Ortega-Castrillon A, Virgo C, Indencleef K, Hoskens H et al. 2020. Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems. Sci. Rep. 10:4443
    [Google Scholar]
  13. 13.
    Richtsmeier JT, Burke Deleon V, Lele SR 2002. The promise of geometric morphometrics. Am. J. Phys. Anthropol. 119:S3563–91
    [Google Scholar]
  14. 14.
    Johnston B, de Chazal P. 2018. A review of image-based automatic facial landmark identification techniques. EURASIP J. Image Video Process. 2018:86
    [Google Scholar]
  15. 15.
    Wu Y, Ji Q 2019. Facial landmark detection: a literature survey. Int. J. Comput. Vis. 127:2115–42
    [Google Scholar]
  16. 16.
    Bodini M. 2019. A review of facial landmark extraction in 2D images and videos using deep learning. Big Data Cogn. Comput. 3:114
    [Google Scholar]
  17. 17.
    Abu A, Ngo CG, Abu-Hassan NIA, Othman SA. 2019. Automated craniofacial landmarks detection on 3D image using geometry characteristics information. BMC Bioinform 19:13548
    [Google Scholar]
  18. 18.
    Vezzetti E, Marcolin F. 2014. 3D landmarking in multiexpression face analysis: a preliminary study on eyebrows and mouth. Aesthet. Plast. Surg. 38:4796–811
    [Google Scholar]
  19. 19.
    Bannister JJ, Crites SR, Aponte JD, Katz DC, Wilms M et al. 2020. Fully automatic landmarking of syndromic 3D facial surface scans using 2D images. Sensors 20:113171
    [Google Scholar]
  20. 20.
    Booth J, Roussos A, Zafeiriou S, Ponniahy A, Dunaway D. 2016. A 3D morphable model learnt from 10,000 faces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)5543–52 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  21. 21.
    de Jong MA, Wollstein A, Ruff C, Dunaway D, Hysi P et al. 2016. An automatic 3D facial landmarking algorithm using 2D Gabor wavelets. IEEE Trans. Image Process. 25:2580–88
    [Google Scholar]
  22. 22.
    de Jong MA, Hysi P, Spector T, Niessen W, Koudstaal MJ et al. 2018. Ensemble landmarking of 3D facial surface scans. Sci. Rep. 8:12
    [Google Scholar]
  23. 23.
    Claes P, Daniels K, Walters M, Clement J, Vandermuelen D, Suetens P. 2012. Dysmorphometrics: the modelling of morphological abnormality. Theor. Biol. Med. Model. 9:5
    [Google Scholar]
  24. 24.
    Amberg B, Romdhani S, Vetter T. 2007. Optimal step nonrigid ICP algorithms for surface registration. 2007 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  25. 25.
    White JD, Ortega-Castrillón A, Matthews H, Zaidi AA, Ekrami O et al. 2019. MeshMonk: open-source large-scale intensive 3D phenotyping. Sci. Rep. 9:6085
    [Google Scholar]
  26. 26.
    Cheng S, Marras I, Zafeiriou S, Pantic M. 2017. Statistical non-rigid ICP algorithm and its application to 3D face alignment. Image Vis. Comput. 58:3–12
    [Google Scholar]
  27. 27.
    Lüthi M, Gerig T, Jud C, Vetter T 2018. Gaussian process morphable models. IEEE Trans. Pattern Anal. Mach. Intell. 40:81860–73
    [Google Scholar]
  28. 28.
    Bahri M, O'Sullivan E, Gong S, Liu F, Liu X et al. 2021. Shape My Face: registering 3D face scans by surface-to-surface translation. Int. J. Comput. Vis. 129:92680–713
    [Google Scholar]
  29. 29.
    Liu F, Tran L, Liu X. 2019. 3D face modeling from diverse raw scan data. 2019 IEEE/CVF International Conference on Computer Vision (ICCV)9407–17 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  30. 30.
    Croquet B, Christiaens D, Weinberg S, Bronstein M, Vandermeulen D, Claes P 2021. Unsupervised diffeomorphic surface registration and non-linear modelling. Medical Image Computing and Computer Assisted Intervention: MICCAI 2021 M de Bruijne, PC Cattin, S Cotin, N Padoy, S Speidel et al. Pap. 12 Cham, Switz.: Springer Int.
    [Google Scholar]
  31. 31.
    Dryden IL, Mardia KV. 1998. Statistical Shape Analysis Chichester, UK: Wiley
  32. 32.
    Cerrolaza JJ, Porras AR, Mansoor A, Zhao Q, Summar M, Linguraru MG. 2016. Identification of dysmorphic syndromes using landmark-specific local texture descriptors. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)1080–83 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  33. 33.
    Kruszka P, Addissie YA, McGinn DE, Porras AR, Biggs E et al. 2017. 22q11.2 deletion syndrome in diverse populations. Am. J. Med. Genet. A 173:4879–88
    [Google Scholar]
  34. 34.
    Kruszka P, Porras AR, Addissie YA, Moresco A, Medrano S et al. 2017. Noonan syndrome in diverse populations. Am. J. Med. Genet. A 173:92323–34
    [Google Scholar]
  35. 35.
    Kruszka P, Porras AR, de Souza DH, Moresco A, Huckstadt V et al. 2018. Williams-Beuren syndrome in diverse populations. Am. J. Med. Genet. A 176:51128–36
    [Google Scholar]
  36. 36.
    Ojala T, Pietikainen M, Maenpaa T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24:7971–87
    [Google Scholar]
  37. 37.
    Dalal N, Triggs B 2005. Histograms of oriented gradients for human detection. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) C Schmid, S Soatto, C Tomasi 886–93 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  38. 38.
    Barina D. 2016. Gabor wavelets in image processing. arXiv:1602.03308 [cs.CV]
  39. 39.
    Boehringer S, Vollmar T, Tasse C, Wurtz RP, Gillessen-Kaesbach G et al. 2006. Syndrome identification based on 2D analysis software. Eur. J. Hum. Genet. 14:101082–89
    [Google Scholar]
  40. 40.
    Loos HS, Wieczorek D, Würtz RP, von der Malsburg C, Horsthemke B 2003. Computer-based recognition of dysmorphic faces. Eur. J. Hum. Genet. 11:8555–60
    [Google Scholar]
  41. 41.
    Saraydemir Ş, Taşpınar N, Eroğul O, Kayserili H, Dinçkan N. 2012. Down syndrome diagnosis based on Gabor wavelet transform. J. Med. Syst. 36:53205–13
    [Google Scholar]
  42. 42.
    Fang S, McLaughlin J, Fang J, Huang J, Autti-Rämö I et al. 2008. Automated diagnosis of fetal alcohol syndrome using 3D facial image analysis. Orthod. Craniofac. Res. 11:3162–71
    [Google Scholar]
  43. 43.
    Suttie M, Wetherill L, Jacobson SW, Jacobson JL, Hoyme HE et al. 2017. Facial curvature detects and explicates ethnic differences in effects of prenatal alcohol exposure. Alcohol. Clin. Exp. Res. 41:81471–83
    [Google Scholar]
  44. 44.
    Baldi P. 2018. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1:181–205
    [Google Scholar]
  45. 45.
    Feng Y, Wu F, Shao X, Wang Y, Zhou X 2018. Joint 3D face reconstruction and dense alignment with position map regression network. Computer Vision—ECCV 2018 V Ferrari, M Hebert, C Sminchisescu, Y Weiss 557–74 Cham, Switz.: Springer Int.
    [Google Scholar]
  46. 46.
    Sharma S, Kumar V. 2020. Voxel-based 3D face reconstruction and its application to face recognition using sequential deep learning. Multimed. Tools Appl. 79:2517303–30
    [Google Scholar]
  47. 47.
    Bronstein MM, Bruna J, LeCun Y, Szlam A, Vandergheynst P. 2017. Geometric deep learning: going beyond Euclidean data. IEEE Signal Process. Mag. 34:418–42
    [Google Scholar]
  48. 48.
    Bronstein MM, Bruna J, Cohen T, Veličković P. 2021. Geometric deep learning: grids, groups, graphs, geodesics, and gauges. arXiv:2104.13478 [cs.LG]
  49. 49.
    Fey M, Lenssen JE, Weichert F, Muller H. 2018. SplineCNN: fast geometric deep learning with continuous B-spline kernels. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition869–77 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  50. 50.
    Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM. 2017. Geometric deep learning on graphs and manifolds using mixture model CNNs. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)5425–34 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  51. 51.
    Bruna J, Zaremba W, Szlam A, LeCun Y. 2014. Spectral networks and locally connected networks on graphs. arXiv:1312.6203 [cs.LG]
  52. 52.
    Gong S, Chen L, Bronstein M, Zafeiriou S. 2019. SpiralNet++: a fast and highly efficient mesh convolution operator. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)4141–48 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  53. 53.
    Hammond P, Suttie M, Hennekam RC, Allanson J, Shore EM, Kaplan FS. 2012. The face signature of fibrodysplasia ossificans progressiva. Am. J. Med. Genet. A 158:61368–80
    [Google Scholar]
  54. 54.
    Matthews HS, Palmer RL, Baynam GS, Quarrell OW, Klein OD et al. 2021. Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism. Sci. Rep. 11:12175
    [Google Scholar]
  55. 55.
    Cootes TF, Hill A, Taylor CJ, Haslam J. 1994. The use of active shape models for locating structures in medical images. Image Vis. Comput. 12:6355–66
    [Google Scholar]
  56. 56.
    Blanz V, Vetter T. 1999. A morphable model for the synthesis of 3D faces. Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques187–94 New York: ACM
    [Google Scholar]
  57. 57.
    Gerig T, Morel-Forster A, Blumer C, Egger B, Luthi M et al. 2018. Morphable face models—an open framework. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)75–82 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  58. 58.
    Bouritsas G, Bokhnyak S, Ploumpis S, Zafeiriou S, Bronstein M. 2019. Neural 3D morphable models: spiral convolutional networks for 3D shape representation learning and generation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV)7212–21 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  59. 59.
    Nauwelaers N, Matthews H, Fan Y, Croquet B, Hoskens H et al. 2021. Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning. Orthod. Craniofac. Res. 24:52134–43
    [Google Scholar]
  60. 60.
    Bagautdinov T, Wu C, Saragih J, Fua P, Sheikh Y. 2018. Modeling facial geometry using compositional VAEs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition3877–86 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  61. 61.
    Allanson J, O'Hara P, Farkas L, Nair R 1993. Anthropometric craniofacial pattern profiles in Down syndrome. Am. J. Med. Genet. 47:5748–52
    [Google Scholar]
  62. 62.
    Bowman AW, Bock MT. 2006. Exploring variation in three-dimensional shape data. J. Comput. Graph. Stat. 15:3524–41
    [Google Scholar]
  63. 63.
    Claes P, Walters M, Gillett D, Vandermeulen D, Clement J, Suetens P. 2013. The normal-equivalent: a patient-specific assessment of facial harmony. Int. J. Oral Maxillofac. Surg. 42:91150–58
    [Google Scholar]
  64. 64.
    Klingenberg C, Wetherill L, Rogers J, Moore E, Ward R et al. 2010. Prenatal alcohol exposure alters the patterns of facial asymmetry. Alcohol 44:7649–57
    [Google Scholar]
  65. 65.
    Hammond P, Forster-Gibson C, Chudley AE, Allanson JE, Hutton TJ et al. 2008. Face–brain asymmetry in autism spectrum disorders. Mol. Psychiatry 13:6614–23
    [Google Scholar]
  66. 66.
    Shaner DJ, Peterson AE, Beattie OB, Bamforth JS. 2000. Assessment of soft tissue facial asymmetry in medically normal and syndrome-affected individuals by analysis of landmarks and measurements. Am. J. Med. Genet. 93:2143–54
    [Google Scholar]
  67. 67.
    Ferrario VF, Sforza C, Poggio CE, Tartaglia G. 1994. Distance from symmetry: a three-dimensional evaluation of facial asymmetry. J. Oral Maxillofac. Surg. 52:111126–32
    [Google Scholar]
  68. 68.
    Klingenberg C, Barluenga M, Meyer A. 2002. Shape analysis of symmetric structures: quantifying variation among individuals and asymmetries. Evolution 56:1909–20
    [Google Scholar]
  69. 69.
    Schoot RA, Hol MLF, Merks JHM, Suttie M, Slater O et al. 2017. Facial asymmetry in head and neck rhabdomyosarcoma survivors. Pediatr. Blood Cancer 64:10e26508
    [Google Scholar]
  70. 70.
    Claes P, Walters M, Shriver M, Puts D, Gibson G et al. 2012. Sexual dimorphism in multiple aspects of 3D facial symmetry and asymmetry defined by spatially dense geometric morphometrics. J. Anat. 221:97–114
    [Google Scholar]
  71. 71.
    Ekrami O, Claes P, White JD, Zaidi AA, Shriver MD, Van Dongen S. 2018. Measuring asymmetry from high-density 3D surface scans: an application to human faces. PLOS ONE 13:12e0207895
    [Google Scholar]
  72. 72.
    Dahary D, Golan Y, Mazor Y, Zelig O, Barshir R et al. 2019. Genome analysis and knowledge-driven variant interpretation with TGex. BMC Med. Genom. 12:200
    [Google Scholar]
  73. 73.
    Heulens I, Suttie M, Postnov A, De Clerck N, Perrotta CS et al. 2013. Craniofacial characteristics of fragile X syndrome in mouse and man. Eur. J. Hum. Genet. 21:8816–23
    [Google Scholar]
  74. 74.
    Hammond P, Hannes F, Suttie M, Devriendt K, Vermeesch JR et al. 2012. Fine-grained facial phenotype–genotype analysis in Wolf-Hirschhorn syndrome. Eur. J. Hum. Genet. 20:33–40
    [Google Scholar]
  75. 75.
    Hammond P, Suttie M. 2012. Large-scale objective phenotyping of 3D facial morphology. Hum. Mutat. 33:5817–25
    [Google Scholar]
  76. 76.
    Tan DW, Foo YZ, Downs J, Finlay-Jones A, Leonard H et al. 2020. A preliminary investigation of the effects of prenatal alcohol exposure on facial morphology in children with Autism Spectrum Disorder. Alcohol 86:75–80
    [Google Scholar]
  77. 77.
    Goodall C. 1991. Procrustes methods in the statistical analysis of shape. J. R. Stat. Soc. B 53:2285–339
    [Google Scholar]
  78. 78.
    Shrimpton S, Daniels K, De Greef S, Tilotta F, Willems G et al. 2014. A spatially-dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction. Forensic Sci. Int. 234:103–10
    [Google Scholar]
  79. 79.
    Roosenboom J, Saey I, Peeters H, Devriendt K, Claes P, Hens G. 2015. Facial characteristics and olfactory dysfunction: two endophenotypes related to nonsyndromic cleft lip and/or palate. BioMed Res. Int. 2015:863429
    [Google Scholar]
  80. 80.
    Muggli E, Matthews H, Penington A, Claes P, O'Leary C et al. 2017. Association between prenatal alcohol exposure and craniofacial shape of children at 12 months of age. JAMA Pediatr 171:8771–80
    [Google Scholar]
  81. 81.
    Rokach L 2010. A survey of clustering algorithms. Data Mining and Knowledge Discovery Handbook O Maimon, L Rokach 269–98 Boston: Springer
    [Google Scholar]
  82. 82.
    Jolliffe IT, Cadima J. 2016. Principal component analysis: a review and recent developments. Philos. Trans. R. Soc. A 374:206520150202
    [Google Scholar]
  83. 83.
    Mutsvangwa T, Douglas TS. 2007. Morphometric analysis of facial landmark data to characterize the facial phenotype associated with fetal alcohol syndrome. J. Anat. 210:2209–20
    [Google Scholar]
  84. 84.
    Hsieh T-C, Bar-Haim A, Moosa S, Ehmke N, Gripp KW et al. 2021. GestaltMatcher: overcoming the limits of rare disease matching using facial phenotypic descriptors. medRxiv 2020.12.28.20248193. https://doi.org/10.1101/2020.12.28.20248193
    [Crossref]
  85. 85.
    van der Maaten L, Hinton G. 2008. Visualizing data using tSNE. J. Mach. Learn. 9:2579–605
    [Google Scholar]
  86. 86.
    Obafemi-Ajayi T, Miles JH, Takahashi TN, Qi W, Aldridge K et al. 2015. Facial structure analysis separates autism spectrum disorders into meaningful clinical subgroups. J. Autism Dev. Disord. 45:51302–17
    [Google Scholar]
  87. 87.
    Suttie M, Foroud T, Wetherill L, Jacobson JL, Molteno CD et al. 2013. Facial dysmorphism across the fetal alcohol spectrum. Pediatrics 131:3e779–88
    [Google Scholar]
  88. 88.
    Agbolade O, Nazri A, Yaakob R, Ghani AA, Cheah YK. 2020. Down syndrome face recognition: a review. Symmetry 12:71182
    [Google Scholar]
  89. 89.
    Hennocq Q, Khonsari RH, Benoît V, Rio M, Garcelon N. 2021. Computational diagnostic methods on 2D photographs: a review of the literature. J. Stomatol. Oral Maxillofac. Surg. 122:4e71–75
    [Google Scholar]
  90. 90.
    Thevenot J, López MB, Hadid A. 2018. A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22:51497–511
    [Google Scholar]
  91. 91.
    Hurst ACE. 2018. Facial recognition software in clinical dysmorphology. Curr. Opin. Pediatr. 30:6701–6
    [Google Scholar]
  92. 92.
    Zhao Q, Okada K, Rosenbaum K, Kehoe L, Zand DJ et al. 2014. Digital facial dysmorphology for genetic screening: hierarchical constrained local model using ICA. Med. Image Anal. 18:5699–710
    [Google Scholar]
  93. 93.
    Song W, Lei Y, Chen S, Pan Z, Yang J-J et al. 2018. Multiple facial image features-based recognition for the automatic diagnosis of turner syndrome. Comput. Ind. 100:85–95
    [Google Scholar]
  94. 94.
    Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP et al. 2014. Diagnostically relevant facial gestalt information from ordinary photos. eLife 3:e02020
    [Google Scholar]
  95. 95.
    Burçin K, Vasif NV. 2011. Down syndrome recognition using local binary patterns and statistical evaluation of the system. Expert Syst. Appl. 38:78690–95
    [Google Scholar]
  96. 96.
    Basel-Vanagaite L, Wolf L, Orin M, Larizza L, Gervasini C et al. 2016. Recognition of the Cornelia de Lange syndrome phenotype with facial dysmorphology novel analysis. Clin. Genet. 89:5557–63
    [Google Scholar]
  97. 97.
    Shukla P, Gupta T, Saini A, Singh P, Balasubramanian R. 2017. A deep learning frame-work for recognizing developmental disorders. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV)705–14 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  98. 98.
    Qin B, Liang L, Wu J, Quan Q, Wang Z, Li D 2020. Automatic identification of Down syndrome using facial images with deep convolutional neural network. Diagnostics 10:7487
    [Google Scholar]
  99. 99.
    Liu H, Mo Z-H, Yang H, Zhang Z-F, Hong D et al. 2021. Automatic facial recognition of Williams-Beuren syndrome based on deep convolutional neural networks. Front. Pediatr. 9:648255
    [Google Scholar]
  100. 100.
    Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N et al. 2019. Identifying facial phenotypes of genetic disorders using deep learning. Nat. Med. 25:60–64
    [Google Scholar]
  101. 101.
    Wilamowska K, Wu J, Heike C, Shapiro L 2012. Shape-based classification of 3D facial data to support 22q11.2DS craniofacial research. J. Digit. Imaging 25:3400–8
    [Google Scholar]
  102. 102.
    Hallgrímsson B, Aponte JD, Katz DC, Bannister JJ, Riccardi SL et al. 2020. Automated syndrome diagnosis by three-dimensional facial imaging. Genet. Med. 22:101682–93
    [Google Scholar]
  103. 103.
    Douglas T, Mutsvangva E. 2009. A review of facial image analysis for delineation of the facial phenotype associated with fetal alcohol syndrome. Am. J. Med. Genet. A 152:528–36
    [Google Scholar]
  104. 104.
    Hammond P, Hutton TJ, Allanson JE, Buxton B, Campbell LE et al. 2005. Discriminating power of localized three-dimensional facial morphology. Am. J. Hum. Genet. 77:6999–1010
    [Google Scholar]
  105. 105.
    O'Sullivan E, van de Lande LS, Papaioannou A, Breakey RWF, Jeelani NO et al. 2021. Craniofacial syndrome identification using convolutional mesh autoencoders. SSRN Preprint, Abstr. 3795325. https://dx.doi.org/10.2139/ssrn.3795325
    [Crossref]
  106. 106.
    Hastie T, Tibshirani R, Friedman J. 2001. The Elements of Statistical Learning New York: Springer
  107. 107.
    Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B. 1998. Support vector machines. IEEE Intell. Syst. Their Appl. 13:418–28
    [Google Scholar]
  108. 108.
    Hart TC, Hart PS. 2009. Genetic studies of craniofacial anomalies: clinical implications and applications. Orthod. Craniofac. Res. 12:3212–20
    [Google Scholar]
  109. 109.
    Hallgrímsson B, Klein O, Spritz R. 2017. Developing 3D craniofacial morphometry data and tools to transform dysmorphology Database, FaceBase Consort. https://www.facebase.org/chaise/record/#1/isa:project/RID=1WWC
  110. 110.
    Marbach F, Rustad CF, Riess A, Đukić D, Hsieh T-C et al. 2019. The discovery of a LEMD2-associated nuclear envelopathy with early progeroid appearance suggests advanced applications for AI-driven facial phenotyping. Am. J. Hum. Genet. 104:4749–57
    [Google Scholar]
  111. 111.
    Oti M, Brunner HG. 2007. The modular nature of genetic diseases. Clin. Genet. 71:11–11
    [Google Scholar]
  112. 112.
    Tidyman WE, Rauen KA. 2009. The RASopathies: developmental syndromes of Ras/MAPK pathway dysregulation. Curr. Opin. Genet. Dev. 19:3230–36
    [Google Scholar]
  113. 113.
    Tzou C-HJ, Rodríguez-Lorenzo A, eds. 2021. Facial Palsy: Techniques for Reanimation of the Paralyzed Face Cham, Switz: Springer Int.
  114. 114.
    Gattani S, Ju X, Gillgrass T, Bell A, Ayoub A. 2020. An innovative assessment of the dynamics of facial movements in surgically managed unilateral cleft lip and palate using 4D imaging. Cleft Palate–Craniofac. J. 57:91125–33
    [Google Scholar]
  115. 115.
    Cohen AS, Cowan T, Le TP, Schwartz EK, Kirkpatrick B et al. 2020. Ambulatory digital phenotyping of blunted affect and alogia using objective facial and vocal analysis: proof of concept. Schizophr. Res. 220:141–46
    [Google Scholar]
  116. 116.
    Briot K, Pizano A, Bouvard M, Amestoy A. 2021. New technologies as promising tools for assessing facial emotion expressions impairments in ASD: a systematic review. Front. Psychiatry 12:530
    [Google Scholar]
  117. 117.
    Trotman CA, Faraway J, Hadlock T, Banks C, Jowett N, Jung HJ. 2018. Facial soft-tissue mobility: baseline dynamics of patients with unilateral facial paralysis. Plast. Reconstr. Surg. Glob. Open 6:10e1955
    [Google Scholar]
  118. 118.
    Samsudin WSW, Sundaraj K, Ahmad A, Salleh H 2016. Initial assessment of facial nerve paralysis based on motion analysis using an optical flow method. Technol. Health Care 24:2287–94
    [Google Scholar]
  119. 119.
    He S, Soraghan JJ, O'Reilly BF. 2007. Biomedical image sequence analysis with application to automatic quantitative assessment of facial paralysis. EURASIP J. Image Video Process 2007:81282
    [Google Scholar]
  120. 120.
    Xu P, Xie F, Su T, Wan Z, Zhou Z et al. 2020. Automatic evaluation of facial nerve paralysis by dual-path LSTM with deep differentiated network. Neurocomputing 388:70–77
    [Google Scholar]
  121. 121.
    Ekman P, Friesen W, Hager J. 2002. FACS Manual Salt Lake City, UT: Netw. Inf. Res. Corp.
  122. 122.
    Hamm J, Kohler CG, Gur RC, Verma R. 2011. Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders. J. Neurosci. Methods 200:2237–56
    [Google Scholar]
  123. 123.
    Baltrušaitis T, Zadeh A, Lim YC, Morency L-P. 2018. OpenFace 2.0: facial behavior analysis toolkit. 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)59–66 Los Alamitos, CA: IEEE Comput. Soc.
    [Google Scholar]
  124. 124.
    Baltrušaitis T, Mahmoud M, Robinson P 2015. Cross-dataset learning and person-specific normalisation for automatic Action Unit detection. 2015 11th IEEE International Conference and Workshops on Automatic Face & Gesture Recognition (FG 2015), Vol. 6 Pap. 2 New York: IEEE
    [Google Scholar]
  125. 125.
    Gudi A, Tasli HE, den Uyl TM, Maroulis A. 2015. Deep learning based FACS Action Unit occurrence and intensity estimation. 2015 11th IEEE International Conference and Workshops on Automatic Face & Gesture Recognition (FG 2015), Vol. 6 Pap. 6 New York: IEEE
    [Google Scholar]
  126. 126.
    Dupré D, Krumhuber EG, Küster D, McKeown GJ. 2020. A performance comparison of eight commercially available automatic classifiers for facial affect recognition. PLOS ONE 15:4e0231968
    [Google Scholar]
  127. 127.
    Terzis JK, Noah ME. 1997. Analysis of 100 cases of free-muscle transplantation for facial paralysis. Plast. Reconstr. Surg. 99:71905–21
    [Google Scholar]
  128. 128.
    House JW, Brackmann DE. 1985. Facial nerve grading system. Otolaryngol. Neck Surg. 93:2146–47
    [Google Scholar]
  129. 129.
    Anguraj K, Padma S. 2012. Analysis of facial paralysis disease using image processing technique. Int. J. Comput. Appl. 54:111–4
    [Google Scholar]
  130. 130.
    Anping S, Guoliang X, Xuehai D, Jiaxin S, Gang X, Wu Z. 2017. Assessment for facial nerve paralysis based on facial asymmetry. Austr. Phys. Eng. Sci. Med. 40:4851–60
    [Google Scholar]
  131. 131.
    He S, Soraghan JJ, O'Reilly BF, Xing D. 2009. Quantitative analysis of facial paralysis using local binary patterns in biomedical videos. IEEE Trans. Biomed. Eng. 56:71864–70
    [Google Scholar]
  132. 132.
    Guo Z, Shen M, Duan L, Zhou Y, Xiang J et al. 2017. Deep assessment process: objective assessment process for unilateral peripheral facial paralysis via deep convolutional neural network. 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)135–38 New York: IEEE
    [Google Scholar]
  133. 133.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S et al. 2015. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Pap. 1 New York: IEEE
    [Google Scholar]
  134. 134.
    Gross MM, Trotman CA, Moffatt KS. 1996. A comparison of three-dimensional and two-dimensional analyses of facial motion. Angle Orthod 66:3189–94
    [Google Scholar]
  135. 135.
    ten Harkel TC, Speksnijder CM, van der Heijden F, Beurskens CHG, Ingels KJAO, Maal TJJ. 2017. Depth accuracy of the RealSense F200: low-cost 4D facial imaging. Sci. Rep. 7:16263
    [Google Scholar]
  136. 136.
    ten Harkel TC, Vinayahalingam S, Ingels KJAO, Bergé SJ, Maal TJJ, Speksnijder CM. 2020. Reliability and agreement of 3D anthropometric measurements in facial palsy patients using a low-cost 4D imaging system. IEEE Trans. Neural Syst. Rehabil. Eng. 28:81817–24
    [Google Scholar]
  137. 137.
    Gerós A, Horta R, Aguiar P. 2016. Facegram—objective quantitative analysis in facial reconstructive surgery. J. Biomed. Inform. 61:1–9
    [Google Scholar]
  138. 138.
    Hallac RR, Feng J, Kane AA, Seaward JR. 2017. Dynamic facial asymmetry in patients with repaired cleft lip using 4D imaging (video stereophotogrammetry). J. Cranio-Maxillofac. Surg. 45:18–12
    [Google Scholar]
  139. 139.
    Desrosiers PA, Bennis Y, Daoudi M, Amor BB, Guerreschi P. 2017. Analyzing of facial paralysis by shape analysis of 3D face sequences. Image Vis. Comput. 67:67–88
    [Google Scholar]
  140. 140.
    Kruszka P, Tekendo-Ngongang C, Muenke M. 2019. Diversity and dysmorphology. Curr. Opin. Pediatr. 31:6702–7
    [Google Scholar]
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