Toward DNA-based facial composites: Preliminary results and validation


      • Facial variation is a complex and multipartite trait that requires proper modeling techniques.
      • Using a genetic basis of 24 SNPs, sex and genomic ancestry we create DNA-based facial composites.
      • Physical accuracy of the predictions is mainly determined by sex and genomic ancestry.
      • The SNP-effects significantly increase the distinctiveness of the predictions.


      The potential of constructing useful DNA-based facial composites is forensically of great interest. Given the significant identity information coded in the human face these predictions could help investigations out of an impasse. Although, there is substantial evidence that much of the total variation in facial features is genetically mediated, the discovery of which genes and gene variants underlie normal facial variation has been hampered primarily by the multipartite nature of facial variation. Traditionally, such physical complexity is simplified by simple scalar measurements defined a priori, such as nose or mouth width or alternatively using dimensionality reduction techniques such as principal component analysis where each principal coordinate is then treated as a scalar trait. However, as shown in previous and related work, a more impartial and systematic approach to modeling facial morphology is available and can facilitate both the gene discovery steps, as we recently showed, and DNA-based facial composite construction, as we show here. We first use genomic ancestry and sex to create a base-face, which is simply an average sex and ancestry matched face. Subsequently, the effects of 24 individual SNPs that have been shown to have significant effects on facial variation are overlaid on the base-face forming the predicted-face in a process akin to a photomontage or image blending. We next evaluate the accuracy of predicted faces using cross-validation. Physical accuracy of the facial predictions either locally in particular parts of the face or in terms of overall similarity is mainly determined by sex and genomic ancestry. The SNP-effects maintain the physical accuracy while significantly increasing the distinctiveness of the facial predictions, which would be expected to reduce false positives in perceptual identification tasks. To the best of our knowledge this is the first effort at generating facial composites from DNA and the results are preliminary but certainly promising, especially considering the limited amount of genetic information about the face contained in these 24 SNPs. This approach can incorporate additional SNPs as these are discovered and their effects documented. In this context we discuss three main avenues of research: expanding our knowledge of the genetic architecture of facial morphology, improving the predictive modeling of facial morphology by exploring and incorporating alternative prediction models, and increasing the value of the results through the weighted encoding of physical measurements in terms of human perception of faces.


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        • Jobling M.A.
        • Gill P.
        Encoded evidence: DNA in forensic analysis.
        Nat. Rev. Genet. 2004; 5: 739-751
        • Butler J.M.
        Fundamentals of Forensic DNA Typing.
        Academic Press, Burlington, USA2009
        • Budowle B.
        • Shea B.
        • Niezgoda S.
        • Chakraborty R.
        Codis str loci data from 41 sample populations.
        J. Forensic Sci. 2001; 46: 453-489
        • Butler J.M.
        Forensic DNA Typing: Biology, Technology, and Genetics of str Markers.
        Academic Press, Burlington2005
        • Kayser M.
        • Schneider P.M.
        DNA-based prediction of human externally visible characteristics in forensics: motivations, scientific challenges, and ethical considerations.
        Forensic Sci. Int. Genet. 2009; 3: 154-161
        • Shriver M.D.
        • Smith M.W.
        • Jin L.
        • Marcini A.
        • Akey J.M.
        • Deka R.
        • Ferrell R.E.
        Ethnic-affiliation estimation by use of population-specific DNA markers.
        Am. J. Hum. Genet. 1997; 60: 957
        • Frudakis T.
        Molecular Photofitting: Predicting Ancestry and Phenotype Using DNA.
        Elsevier, Burlington2010
        • Aeria G.
        • Claes P.
        • Vandermeulen D.
        • Clement J.G.
        Targeting specific facial variation for different identification tasks.
        Forensic Sci. Int. 2010; 201: 118-124
        • Smeets D.
        • Claes P.
        • Vandermeulen D.
        • Clement J.G.
        Objective 3d face recognition: evolution, approaches and challenges.
        Forensic Sci. Int. 2010; 201: 125-132
        • Smeets D.
        • Claes P.
        • Hermans J.
        • Vandermeulen D.
        • Suetens P.
        A comparative study of 3d face recognition under expression variations.
        IEEE Trans. Syst. Man Cybern. C: Appl. Rev. 2012; 42: 710-727
        • Lai K.
        • Samoil S.
        • Yanushkevich S.
        • Collaud G.
        Application of biometric technologies in biomedical systems.
        in: Digital Technologies (DT), 2014 10th International Conference on Digital Technologies, IEEE. 2014
        • Wilkinson C.
        Facial reconstruction – anatomical art or artistic anatomy?.
        J. Anat. 2010; 216: 235-250
        • Claes P.
        • Vandermeulen D.
        • De Greef S.
        • Clement J.G.
        • Willems G.
        • Suetens P.
        Bayesian estimation of optimal craniofacial reconstructions.
        Forensic Sci. Int. 2010; 201: 146-152
        • Claes P.
        • Vandermeulen D.
        • De Greef S.
        • Willems G.
        • Clement J.G.
        • Suetens P.
        Computerized craniofacial reconstruction: conceptual framework and review.
        Forensic Sci. Int. 2010; 201: 138-145
        • Kohn L.
        The role of genetics in craniofacial morphology and growth.
        Annu. Rev. Anthropol. 1991; 20: 261-278
        • Weinberg S.M.
        • Parsons T.E.
        • Marazita M.L.
        • Maher B.S.
        Heritability of face shape in twins: a preliminary study using 3D stereophotogrammetry and geometric morphometrics.
        Dent. 3000. 2013; 1: 7-11
        • Hammond P.
        The use of 3D shape modelling in dysmorphology.
        Arch. Dis. Child. 2007; 92: 1120-1126
        • Baynam G.
        • Walters M.
        • Claes P.
        • Kung S.
        • Le Souef P.
        • Dawkins H.
        • Gillett D.
        • Goldblatt J.
        The facial evolution: looking backwards and moving forward.
        Hum. Mutat. 2013; 34: 14-22
        • Hopman S.M.
        • Merks J.H.
        • Suttie M.
        • Hennekam R.C.
        • Hammond P.
        Face shape differs in phylogenetically related populations.
        Eur. J. Hum. Genet. 2014;
        • Claes P.
        • Walters M.
        • Shriver M.D.
        • Puts D.A.
        • Gibson G.
        • Clement J.G.
        • Baynam G.
        • Verbeke G.
        • Vandermeulen D.
        • Suetens P.
        Sexual dimorphism in multiple aspects of 3d facial symmetry & asymmetry defined by spatially dense geometric morphometrics.
        J. Anat. 2012; 221: 97-114
        • Paternoster L.
        • Zhurov A.I.
        • Toma A.M.
        • Kemp J.P.
        • Pourcain B.
        • Timpson N.J.
        • McMahon G.
        • McArdle W.
        • Ring S.M.
        • Smith G.D.
        • Richmond S.
        • Evans D.M.
        Genome-wide association study of three-dimensional facial morphology identifies a variant in pax3 associated with nasion position.
        Am. J. Hum. Genet. 2012; 90: 478-485
        • Liu F.
        • van der Lijn F.
        • Schurmann C.
        • Zhu G.
        • Chakravarty M.M.
        • Hysi G.
        • Wollstein A.
        • Lao C.
        • de Bruijne M.
        • Ikram M.A.
        • Van der Lugt A.
        • Rivadeneira F.
        • Uitterlinden A.G.
        • Hofman A.
        • Niessen W.J.
        • Homuth G.
        • de Zubicaray G.
        • McHanon K.L.
        • Thompson P.M.
        • Daboul A.
        • Puls R.
        • Hegenscheid K.
        • Bevan L.
        • Pausova Z.
        • Medland S.E.
        • Montgomery G.W.
        • Wright M.J.
        • Wicking C.
        • Boehringer S.
        • Spector T.D.
        • Paus T.
        • Martin N.G.
        • Biffar R.
        • Kayser M.
        A genome-wide association study identifies five loci influencing facial morphology in europeans.
        PLoS Genet. 2012; 8: e1002932
        • Peng S.
        • Tan J.
        • Hu S.
        • Zhou H.
        • Guo J.
        • Jin L.
        • Tang K.
        Detecting genetic association of common human facial morphological variation using high density 3d image registration.
        PLoS Comput. Biol. 2013; 9: e1003375
        • Claes P.
        • Liberton D.K.
        • Daniels K.
        • Rosana K.M.
        • Quillen E.E.
        • Pearson L.N.
        • McEvoy B.
        • Bauchet M.
        • Zaidi A.A.
        • Yao W.
        • Tang H.
        • Barsh G.S.
        • Absher D.M.
        • Puts D.A.
        • Rocha J.
        • Beleza S.
        • Pereira R.W.
        • Baynam G.
        • Suetens P.
        • Vandermeulen D.
        • Wagner J.K.
        • Boster J.S.
        • Shriver M.D.
        Modeling 3d facial shape from DNA.
        PLoS Genet. 2014; 10: e1004224
        • Claes P.
        • Walters M.
        • Clement J.G.
        Improved facial outcome assessment using a 3d anthropometric mask.
        Int. J. Oral Maxillofac. Surg. 2012; 41: 324-330
        • Claes P.
        • Walters M.
        • Vandermeulen D.
        • Clement J.G.
        Spatially dense 3d facial asymmetry assessment in both typical and disordered growth.
        J. Anat. 2011; 219: 444-455
        • Rohlf F.
        • Slice D.
        Extensions of the procrustus method for the optimal superimposition of landmarks.
        Syst. Zool. 1990; 39: 40-59
        • Mardia K.V.
        • Bookstein F.L.
        • Moreton I.J.
        Statistical assessment of bilateral symmetry of shapes.
        Biometrika. 2000; 87: 285-300
        • Klingenberg C.P.
        • Barluenga M.
        • Meyer A.
        Shape analysis of symmetric structures: quantifying variation among individuals and asymmetry.
        Evolution. 2002; 56: 1909-1920
        • Shrimpton S.
        • Daniels K.
        • De Greef S.
        • Tilotta F.
        • Willems G.
        • Vandermeulen D.
        • Suetens P.
        • Claes P.
        A spatially dense regression study of facial form and tissue depth: towards an interactive tool for craniofacial reconstruction.
        Forensic Sci. Int. 2014; 234: 103-110
        • Anderson M.J.
        • Legendre P.
        An empirical comparison of permutation methods for tests of partial regression coefficients in a linear model.
        J. Stat. Comput. Simul. 1999; 62: 271-303
        • Brennan S.E.
        Caricature generator: the dynamic exaggeration of faces by computer.
        Leonardo. 1985; 18: 170-178
        • Dal U.
        • Dal D.
        • Abraham S.
        A facial caricature generation system using adaptive thresholding.
        in: Information and Communication Technologies (WICT), 2011 World Congress on 2011: IEEE. 2011
        • Light L.L.
        • Kayra-Stuart F.
        • Hollander S.
        Recognition memory for typical and unusual faces.
        J. Exp. Psychol. [Hum. Learn]. 1979; 5: 212
        • Aldhous P.
        Genetic mugshot recreates faces from nothing but DNA.
        New Sci. 2014;
        • Liu F.
        • van Duijn K.
        • Vingerling J.R.
        • Hofman A.
        • Uitterlinden A.G.
        • Janssens A.
        • Kayser M.
        Eye color and the prediction of complex phenotypes from genotypes.
        Curr. Biol. 2009; 19: R192-R193
        • Kastelic V.
        • Pośpiech E.
        • Draus-Barini J.
        • Branicki W.
        • Drobnič K.
        Prediction of eye color in the slovenian population using the irisplex snps.
        Croat. Med. J. 2013; 54: 381-386
        • Kayser M.
        • Liu F.
        • Janssens A.
        • Rivadeneira F.
        • Lao O.
        • van Duijn K.
        • Vermeulen M.
        • Arp P.
        • Jhamai M.M.
        • van IJcken W.F.
        Three genome-wide association studies and a linkage analysis identify HERC2 as a human iris color gene.
        Am. J. Hum. Genet. 2008; 82: 411-423
        • Ober U.
        • Ayroles J.F.
        • Stone E.A.
        • Richards S.
        • Zhu D.
        • Gibbs R.A.
        • Stricker C.
        • Gianola D.
        • Schlather M.
        • Mackay T.F.
        Using whole-genome sequence data to predict quantitative trait phenotypes in drosophila melanogaster.
        PLoS Genet. 2012; 8: e1002685
        • Gondro C.
        • Van der Werf J.
        • Hayes B.
        Genome-Wide Association Studies and Genomic Prediction.
        Springer, New York2013
        • Boehringer S.
        • van der Lijn F.
        • Liu F.
        • Gunther M.
        • Sinigerova S.
        • Nowak S.
        • Ludwig K.U.
        • Herberz R.
        • Klein S.
        • Hofman A.
        • Uitterlinden A.G.
        • Niessen W.J.
        • Breteler M.M.B.
        • van der Lugt A.
        • Wurtz R.P.
        • Nothen M.M.
        • Horsthemke B.
        • Wieczorek D.
        • Mangold E.
        • Kayser M.
        Genetic determination of human facial morphology: links between cleft-lips and normal variation.
        Eur. J. Hum. Genet. 2011; 19: 1192-1197
        • Fu G.
        • Bo W.
        • Pang X.
        • Wang Z.
        • Chen L.
        • Song Y.
        • Zhang Z.
        • Li J.
        • Wu R.
        Mapping shape quantitative trait loci using a radius-centroid-contour model.
        Heredity. 2013; 110: 511-519
        • Bo W.
        • Wang Z.
        • Xu F.
        • Fu G.
        • Sui Y.
        • Wu W.
        • Zhu X.
        • Yin D.
        • Yan Q.
        • Wu R.
        Shape mapping: genetic mapping meets geometric morphometrics.
        Brief. Bioinform. 2013; : bbt008
        • Stephan C.N.
        • Cicolini J.
        The reproducibility of facial approximation accuracy results generated from photo-spread tests.
        Forensic Sci. Int. 2010; 201: 133-137
        • Stephan C.
        The accuracy of facial “reconstruction”: a review of the published data and their interpretive value.
        Minerva Medicoleg. 2009; 129: 47-60
        • Claes P.
        • Vandermeulen D.
        • De Greef S.
        • Willems G.
        • Suetens P.
        Craniofacial reconstruction using a combined statistical model of face shape and soft tissue depths: methodology and validation.
        Forensic Sci. Int. 2006; 159: 147-158
        • Hill H.
        • Claes P.
        • Crocoran M.
        • Walters M.
        • Johnston A.
        • Clement J.G.
        How different is different? Criterion and sensitivity in face-space.
        Front. Psychol. Percept. Sci. 2011; 2: 1-14
        • Lee K.
        • Byatt G.
        • Rhodes G.
        Caricature effects, distinctiveness, and identification: testing the face-space framework.
        Psychol. Sci. 2000; 11: 379-385