• three dimensional;
  • facial analysis;
  • phenotype;
  • rare diseases;
  • dysmorphology;
  • vaccines;
  • evolution


  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

Three-dimensional  (3D) facial analysis is ideal for high-resolution, nonionizing, noninvasive objective, high-throughput phenotypic, and phenomic studies. It is a natural complement to (epi)genetic technologies to facilitate advances in the understanding of rare and common diseases. The face is uniquely reflective of the primordial tissues, and there is evidence supporting the application of 3D facial analysis to the investigation of variation and disease including studies showing that the face can reflect systemic health, provides diagnostic clues to disorders, and that facial variation reflects biological pathways. In addition, facial variation has been related to evolutionary factors. The purpose of this review is to look backward to suggest that knowledge of human evolution supports, and may instruct, the application and interpretation of studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease. Furthermore, in the context of advances of deep phenotyping and data integration, to look forward to suggest approaches to scalable implementation of facial analysis, and to suggest avenues for future research and clinical application of this technology.


  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

Imaging is  ideal for phenomic studies, which involve the acquisition of high-dimensional phenotypic data on an organism-wide scale, as it spans molecular to whole organism spatial registers [Walter et al., 2010]. Phenomic studies are the natural complement to (epi)genetic technologies to facilitate advances in biology as phenomic data are required to understand which genomic variants affect phenotypes, to investigate pleiotropy, and to unravel complex phenomena including health and evolutionary fitness [Houle et al., 2010]. Houle et al. (2010) suggested that a coalition of factors support the timeliness of consideration of phenomic technologies. These include the increasing availability and rapid emergence of analytical and bioinformatic approaches; progress in the dynamic integration of phenomena at multiple levels from genes through to whole organisms; and that, in many cases, phenotypic data are the most powerful predictors of important biological outcomes. In addition, the complexity of genetic causation supports the integration of phenomics with other -omics technologies to investigate causal networks.

The face is uniquely reflective of the craniofacial primordia (see Fig. 1) and it can reflect systemic health, provide diagnostic clues to disorders, and there is evidence supporting the application of three-dimensional (3D) facial analysis to the study of variation and disease [Baynam et al., 2011, 2012; Hammond et al., 2012]. Some known and prospective roles for large-scale objective assessment of facial morphology have recently been elegantly described [Hammond and Suttie, 2012] as part of a special issue on deep phenotyping in this journal. The purpose of this review is, first, to look backward to suggest that knowledge of human evolution supports, and may instruct, the application of objective high-resolution studies of facial morphology for documentation of human variation and investigation of its relationships with health and disease; vaccine responses will be used for illustrative purposes. Second, this review will look forward to suggest approaches to scalable implementation of facial analysis, and to propose some avenues for future research that ultimately may have clinical implications.


Figure 1. Development of the craniofacial primordia. (AD) Representations of frontal views of mouse embryos showing the prominences that give rise to the main structures of the face. The frontonasal (or median nasal) prominence (vertical lines) gives rise to the forehead (A), the middle of the nose (B), the philtrum of the upper lip (C) and the primary palate (D), whereas the lateral nasal prominence (hashed lines) forms the sides of the nose (B, D). The maxillomandibular prominences (horizontal lines) give rise to the lower jaw (specifically from the mandibular prominences), to the sides of the middle and lower face, to the lateral borders of the lips, and to the secondary palate (from the maxillary prominences). E: Frontal view of a chick embryo, also showing which prominences give rise to different facial structures. F: Frontal view of a human child, with different facial structures coded to indicate the prominences from which each structure developed.

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Evolutionary Influences: Looking Backward

  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

Vaccine Responses as an Example of Evolutionary Influences on Phenotype

Vaccination is unequivocally important in prevention of infectious diseases and there are increasing applications to noninfectious diseases. Vaccination responses are complex immunogenetically mediated phenotypes, which may be representative of phenomena relevant to other immunologically mediated phenotypes. A nexus of factors support interrelationships between vaccine responses, evolution, and facial phenotypes; and that these can now be assessed with robust, sensitive, and scalable methods. Notably: (1) associations between candidate genes selected on an evolutionary basis and vaccine responses have been described [Baynam, 2008; Baynam, et al., 2007a, 2008]; (2) genetic diversity of key immunoregulatory loci are revealed in human faces [Lie et al., 2008]; (3) facial phenotypes have been implicated in mate selection preferences [Lie et al., 2008] and these processes influence evolution; (4) the face is uniquely reflective of all the primordial tissues; (5) 3D facial analysis has been successfully employed to elucidate biological pathways [Tobin et al., 2008]; and (6) advances in imaging systems and algorithms continue to improve the ability of 3D facial analysis to explore facial variation [Claes et al., 2010a,b, 2011b, 2012b].

There are a number of factors mediating vaccine responses and their ontogeny. These include, but are not limited to, genetic factors, gender effects, gene–gender effects, and epigenetics.


The importance of genetic influences is suggested by differences in vaccine responses between individuals and ethnic groups [Poland and Jacobson, 1998], high heritability of specific antibody and Th2 cytokine responses to vaccines [Hohler et al., 2002; Ovsyannikova et al., 2004; Tan et al., 2001], and genetic association studies (reviewed in [Blackwell et al., 2009; Yucesoy et al., 2009]). Notably, associations of cytokine gene variations with known relationships with atopy, which were selected within an evolutionary framework [Le Souef et al., 2000] and that interact within biological pathways [Wiertsema et al., 2007], have been shown with vaccine responses [Baynam et al., 2007a,b, 2008; Wiertsema et al., 2007].


Gender is an important influence on disease and vaccination. Gender differences in measles vaccine antibody responses have been shown in adults and children [Benn et al., 1997; Green et al., 1994]. In addition, gender-dependent postmeasles vaccination differences in T-cell proliferation [Leon et al., 1993], side effects [Shohat et al., 2000], and mortality from various diseases have been reported [Aaby et al., 1993, 1993,b; Holt et al., 1993; Weiss, 1992]. Gender-dependent variation extends outside measles vaccination; for example, a herpes simplex virus vaccine was more efficacious in women than in men [Zhang et al., 2008].

Gene–gender interactions

Gender influences gene expression at X-chromosome and autosomal loci [Delongchamp et al., 2005; Kim et al., 2006; Lyon, 1963; Talebizadeh et al., 2006], gender-dependent genetic effects have been reported for disorders mediated by immunological and inflammatory mechanisms [Han et al., 2008; Rana et al., 2007] and they have been described for vaccine responses [Baynam et al., 2008; Gordeeva et al., 2006]. Children of different genders were found to have characteristic HLA DR markers of humoral response to diphtheria toxoid and measles vaccine [Gordeeva et al., 2006] and gender-dependent associations of cytokine gene alleles have been described with responses to these vaccines [Baynam et al., 2008]. In the latter study, the cytokine genotypes that were investigated had been previously associated with atopy, were originally selected on an evolutionary basis, and had been associated with altered vaccine responses [Baynam et al., 2007b; Wiertsema et al., 2007].


Epigenetics is the study of heritable changes in gene expression unrelated to DNA sequence changes. Progress in this field is expanding our knowledge of developmental biology and disease, and epigenetic mechanisms have been proposed to modulate gender-dependent genetic effects [Bottema et al., 2005] and gene–environment interaction effects of vaccine responses [Baynam et al., 2007a].

One of the major epigenetic mechanisms, methylation of CpG dinucleotides, regulates cytokine gene transcription (reviewed in [Wilson et al., 2005]) and it has been shown to have a gender differential [Sarter et al., 2005]. Promoters are enriched for CpG dinucleotides [Gardiner-Garden and Frommer, 1987] and gender-dependent genotype effects have been reported in association with promoter polymorphisms [Bartfai et al., 2003; Cheong et al., 2005; Karjalainen et al., 2002, 2003; Lio et al., 2002; Okayama et al., 2005; Szczeklik et al., 2004]. Methylation is also integral to the prototypical gender-specific genetic process of X-inactivation [Panning and Jaenisch, 1996], an example of monoallelic exclusion, that is, the process of expressing only one of two alleles of a gene, which has been implicated in regulation of immune genes, including cytokine genes [Bayley et al., 2003; Matesanz et al., 2000]. Therefore, gender can exert its effects through epigenetic influences, which may be mediated by methylation and monoallelic exclusion.

It is also notable that a genome wide association study on multiple tissues suggested that gender influenced methylation of autosomes, as well as sex chromosomes. It identified clusters of autosomal genes methylated differentially by sex; this included genes expected to influence immunological processes. On the basis of these findings, the authors suggested that controlling for gender is important when investigating the effects of methylation and that these effects may be entangled with nongenetic factors [Liu et al., 2010].


Exposures, and other nongenetic factors, that act in early life are likely to interact with a child's genotype to modulate responses to other nongenetic factors [Hoffjan et al., 2005]. Accordingly, the probability of detecting genetic effects is increased when analyses account for important nongenetic factors [Baynam et al., 2007a; Colilla et al., 2003; Wiertsema et al., 2006] and when these investigations are pursued within a biological framework of, in this instance, vaccine response ontogeny. The flexibility of cytokine gene expression may be greater earlier in development [Lohning et al., 2002] and therefore relationships between gender, genes, and vaccine responses may be optimally analysed in early childhood [Baynam et al., 2008]. In this context, it is noteworthy that cord blood cytokine responses predict childhood vaccine responses and associate with cytokine genotypes that include those with demonstrated gene–gender interactions [Baynam, 2008].

Although studies in early life may provide the most fertile ground for unmasking interaction effects and their possible epigenetic basis, studies in the elderly may also provide further insights. Accordingly, epigenetic marks may be most divergent in the elderly [Martin, 2005] and gender-dependent effects of IL-10 have been described in an elderly cohort [Caruso et al., 2004].

A Direction for Further study: 3D Facial Analysis

Evolutionary pressures may shape patterns of vaccine responses and phenomic technologies are providing novel avenues to explore these phenomena.

The efficacy of (epi)genetic studies are reliant on accurate phenotyping. A number of approaches to the measurement and assessment of facial form exist, for the interested reader, a brief summary of facial biometrics is found in the online Supporting Information (Supp. Tables S1 and S2). Emerging techniques utilizing 3D facial scanning and geometric morphometric analysis of high-resolution scan data are providing objective and automated means to identify subtle facial variation, and studies employing 3D facial analysis have offered novel approaches to examine cell biology [Tobin et al., 2008]. Current approaches using tens of thousands of data points from individual faces, which are compared with reference normal population data sets, are the foundation of these studies [Hammond et al., 2004]. Innovative approaches have been developed to account for “normal” facial variations that can impact on their discriminatory power. These approaches can be tailored to the assessment of facial phenotypes including facial asymmetry [Claes et al., 2011a]. For example, to provide a measure of discrepancy in form, including asymmetry, the degree, distribution, and locality of discordance can be calculated and visualized by color histogram mapping of an individual's scans (Figs. 2a and 2b).


Figure 2. A: Dysmorphometric analysis of an individual with oculoauriculovertebral spectrum (OAVS). (A) Individual's facial presentation. (B) “Normal Equivalent” face: the harmonious counterpart to the individual's facial presentation. (C) Vector field of discordant regions (scale bar in millimeters) generated from robust superimposition of the mapped facial scan and its harmonious counterpart “normal equivalent.” The discordance covered 13% of the facial surface (relative significant discordance [RSD] score of 13%)) and there was a summary severity score of 1.89 mm (root-mean-squared error [RMSE]). B: Facial asymmetry analysis of an individual with OAVS. (A) oblique (B) frontal, (C) and worm's eye vector fields (scale bar in millimeters). Images are generated from the robust superimposition of a mapped facial scan and its mapped reflected manifold to establish spatially dense correspondence. The asymmetry discordance covered 19% of the facial surface (relative significant asymmetry [RSA] score of 19%) with summary severity score of 4.8 mm (RMSE).

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Asymmetry, gender, (epi)genetics, and complex phenotypes

Although multiple facial phenotypes might correlate with immunological outcomes, asymmetry is a promising candidate. Facial asymmetry is described in a large number of genetic syndromes; a search of the Possum dysmorphology database © using the term facial asymmetry yields 239 conditions. A combined search with immunological dysfunction yields 39 results (database). Intriguingly, a number of these conditions, for instance, CHARGE syndrome and Deletion 22q11 disorder, are known to be associated with immunological compromise, including impaired vaccination responses, which generally improves with age. In addition, some of these conditions are known to involve epigenetic mechanisms. Notably, the CHD7 gene, in which mutations are identified in the majority of individuals with CHARGE syndrome, has a role in chromatin remodeling [Lalani et al., 2009] which is a fundamental epigenetic process [Feinberg, 2010]. Other disorders associated with facial asymmetry involve genes, such as those of the RAS-MAPK network, that are in developmental pathways important to cell lineage determination, growth, and differentiation (Genecards). Furthermore, components of the RAS-MAPK pathway may influence immunologically mediated phenotypes [Lund et al., 2007].

Facial and somatic asymmetries have been implicated in evolutionary processes and evolutionary processes are suggested to influence the pathogenesis of complex diseases [Le Souef et al., 2000]. Therefore, accurate determination of facial asymmetry may provide novel data for investigating complex phenotypes such as vaccine responses. Facial asymmetries have been implicated in mate selection preferences [Lie et al., 2008], which are known to influence evolution. In this context, symmetry in women, which correlated with non-MHC loci variation, has been established as an attractive trait as rated by men [Lie et al., 2008].  Also, in subjective comparisons of monozygotic twin pairs, the twin with perceived facial symmetry was rated the more attractive; this perceived attractiveness was directly related to the magnitude of the asymmetry [Mealey et al., 1999]; a biological underpinning for the relationship between symmetry and attractiveness may be the association of somatic symmetry and female fertility [Jasienska, 2006]. Studies also suggested an interactive effect between MHC variation and facial attractiveness on mate choice [Lie et al., 2008], and MHC variation is associated with many immunologically mediated diseases, in addition to resistance to infections. However, relationships between genetic factors and facial attractiveness are not limited to the MHC and there are gender-dependent differences [Lie et al., 2008]. Considering non-MHC loci, studies of cord blood T cells identified that the IL-4 cytokine gene induces components of the RAS-MAPK pathway [Lund et al., 2007]. Mutations in genes in this pathway occur in Noonan syndrome [Allanson, 2008], and facial asymmetry is one of the principal components of facial variation of this condition [Hammond et al., 2004].

Human facial asymmetry differs between genders [Claes et al., 2012c; Ercan et al., 2008], its magnitude is age dependent (unpublished observation) and it may be greatest earlier in life. Given that (1) there are age and gender-dependent variations in asymmetry; (2) facial asymmetry may have evolutionary foundations; and (3) these factors parallel aspects of immune system ontogeny, it is possible that factors mediating facial asymmetry may converge with those modulating vaccine responses.

The above observations may have relevance for other complex phenotypes. Studies on neurocognitive phenotypes with pathogenetic overlap, including schizophrenia and autism, demonstrated that (1) asymmetry varies with cognition in a gender-dependent manner [Hennessy et al., 2006]; (2) gender-specific facial asymmetry occurs in schizophrenia [Hennessy et al., 2004]; and (3) gender-dependent face–brain asymmetry has been identified in schizophrenia [Hammond et al., 2008]. In addition, tobacco, a known modifier of vaccine responses, both in isolation and as part of genetic interactions [Baynam et al., 2007a], influences the relationship between schizotypy, a mild nonclinical thinking style reminiscent of the one reported by individuals with a clinical diagnosis of schizophrenia, and hemispheric asymmetry [Herzig et al., 2010].

The interplay of factors mediating vaccine responses and facial biology support the proposition that high-resolution 3D facial phenotyping could be used to explore mechanisms mediating vaccine responses [Baynam et al., 2012] and other complex phenotypes. The relatively low cost of 3D facial analysis, including the absence of consumables, make it a suitable addition to existing cohorts and offers advantages that may be particularly relevant to research in resource poor countries.

Moving Forward: The evolution of Facial Analysis

  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

Understanding Normal Variation

The acquisition and understanding of normative data are the foundations for investigating the relationships between facial variation, health, and disease. A number of extant and evolving geographically dispersed datasets with variable characteristics [Hammond and Suttie, 2012] have been ascertained and include the Perth Face-Space which, when combined with related disorder sets, consists of an expanding set of approximately 1,500 imaged individuals. Optimally, this information would be coordinated in a global nodal network with common data elements; and to initiate this process, existing datasets and infrastructure should be consolidated and coordinated.

To understand normative variation, population groups underrepresented in current datasets would be included. This mirrors the situation with genetic studies that often exclude these populations, such as Indigenous Australians, who thus do not derive the full benefit of biomedical innovation. Addressing these issues may help to avoid the perpetuation of health disparities in these populations [Baynam, 2012; Kowal et al., 2011]. Furthermore, considering developmental considerations, longitudinal studies initiated in early childhood will be important and, by corollary, studies in elderly populations may also provide unique insights.

A Focus on Rare Diseases

Each rare disease (RD) is, by definition, of low prevalence. However, because there are 5,000–8,000 RDs, when taken cumulatively, the number of affected has been estimated at 1 per 10–18 people, or about 6–10% of the population [Donaldson, 2009; Knight and Senior, 2006; Minister of Health and Social Protection et al., 2004]. This cumulative prevalence equates to an estimated 1.4 to 2 million Australians, including more than 400,000 children and 27 million Europeans. Approximately, 80% of RDs are monogenetic or have a strong genetic basis, and some rare genetic syndromes have characteristic facies that have diagnostic utility. Accordingly, a number of studies have objectively documented facial phenotypes of individual RDs [Cox-Brinkman et al., 2007; Hammond et al., 2004, 2012; Tobin et al., 2008]. This work may form the foundation for developing tools that facilitate disease diagnostics, screening, differential diagnosis, and monitoring of therapy.

Normative studies of young individuals and of those affected with RD will be of paramount importance as an estimated 65% of RD appear in early life [Minister of Health and Social Protection et al., 2004] and timely diagnosis is central to optimal outcomes. Factors supporting facial analysis as particularly suitable for very young individuals include (1) it poses no health risk; (2) capture time is fast in comparison to other medical imaging; and (3) if required, image capture can be easily repeated until a suitable image is obtained [Kung et al., 2012].

Novel approaches to 3D facial analysis, including the normal equivalent, have been developed that approach the issue of the rarity of individual RD [Claes et al., 2010b,c; 2012a,b; Hammond and Suttie, 2012]. These techniques allow for a more individualized assessment of facial dysmorphology. In an exploratory study, one of these techniques, dysmorphometrics was used to establish a facial signature for a rare and treatable disorder that had variable facial severity proportional to the clinical disease state. This finding will potentially provide adjunctive evidence for noninvasively monitoring treatment response in this condition [Kung et al., 2012]. In 2011, 33% of marketed innovative medicinal products in the United States were intended for an RD (FDA, 2011). With the expansion of RD therapeutics, developments in treatment monitoring will be required. Equally, for those disorders for which treatments are not currently available these techniques will be important for objective natural history studies. The fine-scale, objective, scalable, cost-efficient, noninvasive, nonirradiating, and relatively portable nature of 3D facial analyses make these technologies unique candidates for these applications. It is likely that similar methodologies can be extended to nonfacial imaging, for example, for assessment of disproportion, and other aspects of habitus, by whole body scanning in those with skeletal dysplasias; or assessment of digital changes in the mucoploysaccharidoses. Additional opportunities are provided by the ability for joint investigations with traditional imaging technologies. For example, paired with MRI for face–brain studies [Hammond and Suttie, 2012] or with cone–beam CT for skull–face assessments [Cheung et al., 2011]. Lastly, the relative inexpensiveness of this technology makes it suitable to provide phenotypic data to existing disease registries.

Understanding individual RDs has provided fundamental insights into basic biological processes and into the causes of common diseases. A recent example is the perspective provided from relationships between a rare lysosomal storage disorder, Gaucher disease, and the more common Parkinson's disease, which may yield a specific therapeutic approach to Parkinson's disease and related conditions [Mazzulli et al., 2011]; 3D facial analysis of RD may ultimately benefit common diseases. Along related lines, by endophenotyping and/or by elucidating biological pathways, a more speculative use of 3D facial analysis may be to assist with drug rescue, that is, research involving discontinued drugs or candidate drugs that are not currently in development, and drug repurposing, that is, research on approved drugs for new indications. Pharmaceutical companies and biotechs might be able to employ 3D facial analysis research to scan their libraries for promising drug compounds or investigate new drug entities. These screening applications may be a cost-effective mechanism compared to current new drug entity discovery which equates to over $1 billion dollars per new drug [Dimasi and Grabowski, 2007]. This approach may be particularly relevant given the incentives and regulatory environment surrounding orphan drug development.

Human 3D facial analysis, when combined with other technologies, including facial analysis of mice, has been used to investigate disease biology [Tobin et al., 2008]. Between species (e.g., mouse–human) comparative phenotypic assessments will yield further insights into disease biology and such comparisons will be facilitated by Mouse phenotypic, and genetic, resources that are being coordinated by the International Mouse phenotyping consortium (IMPC) and related projects [Chen et al., 2012]. Notably, the IMPC draft phenotyping pipeline includes dysmorphology [Schofield et al., 2012]. By the way of a further example of the potential of cross-species comparisons, recently a study demonstrated large-scale objective association of mouse phenotypes with human symptoms through structural variation indentified in patients with developmental disorders [Boulding and Webber, 2012]. Furthermore, explorations of disease biology, and other potential applications, may be aided by emerging methods that facilitate between-study integration, interrogation, visualization, and exchange of phenotypic and genotypic information [Adamusiak et al., 2012; de Bono et al., 2012; Pan et al., 2012].

It has been suggested that the most important responsibility of the physician is to observe the phenotype of their patients, and this has become particularly relevant in an era where genotype–phenotype relationships are rapidly being clarified by the use of high throughput molecular technologies. Regarding these genotype–phenotype investigations: methods for capturing and analyzing phenotypic data are one of the major bottlenecks in the understanding of human genome biology [Robinson, 2012]; next-generation sequencing (NGS) will require next generation phenotyping (NGP) [Baynam, 2012; Hennekam and Biesecker, 2012]; the full potential of phenome databases, such as OMIM, requires the aggregation of phenotypic information from multiple databases which depends on the availability of fine-grained phenotype ontology and feature frequency data [Oti et al., 2009]; and delineating the phenotypic components of disease allows (1) relationships between gene function and phenotype to be established, where one aspect of the phenotype is common to several genes in the same pathway, and (2) the discovery of interrelationships between disease pathways and genetic networks [Schofield and Hancock, 2012]. NGS has been described as the most powerful diagnostic tool developed since the roentgenogram [Hennekam and Biesecker, 2012]. Coupling NGP methods such as 3D facial analysis with NGS, and other -omics technologies, will enhance the use of these technologies in delineating disease causation. These factors support 3D facial analysis contributing toward reductions in the burden of RDs. In addition, the objective nature of 3D facial analysis may compliment efforts to develop standardized descriptions of human phenotypic variation, such as the Elements of Morphology Project, that, in partner with standardized molecular nomenclatures, aim to facilitate robust genotype-phenotype correlations [Carey et al., 2012]; incorporation of phenotypic information into genomic variation databases may enhance clinical care and research [Riggs et al., 2012].

Synergies and Future Directions

Unique aspects of 3D facial analysis provide the potential to develop traditional and novel collaborative networks. In addition to existing fruitful collaborations between Computer Engineers, Clinical Geneticists, Paediatricians, Dentists and Surgeons, networks could be expanded to include a diverse range of medical professionals and scientists. The engineered bases of these techniques will also benefit from progress within the greater bioinformatic community. Further input may be provided from commercial bodies, for example, noting the similarities between financial risk analysis and diagnostic algorithms, and between geological surface analysis and analysis of human surfaces.

There is a relative dearth of genetic and phenotypic information in some developing nations and amongst some populations, by virtue of geographic isolation or other factors. The low-cost and portability of 3D scanners can address these issues.

When assessing an individual's facial morphology, Clinical Geneticists often “subtract” familial variation obtained from direct observation of a consultand's relatives or familial photographs. The ability to develop objective familial imaging to assess this, that is, “familial morphometrics,” will improve the sensitivity and specificity of 3D facial analysis. In general, morphometric techniques, an object's or organism's form (a concept that encompasses size and shape independent from orientation or position) is the structural information and the conventional observation of interest. These observations, however, are made in isolation and a context-based observation of shape currently does not exist. The introduction of context information as in computational linguistics [Qiu et al., 2011], can lead to an improved understanding of observations made. The idea is to combine contextual information with structural information already coded in the shape of an individual's face through the relationship of that individual's facial morphology with that of its parents. Such a combined contextual and structural model is novel within morphometrics and allows for unique solutions, including for applications to sparse and rare datasets. These analyses will need to be distinct to independent assessments of family members, as recognizing, rather than controlling for, parental facial variation may provide its own insights. For instance, although the significance is currently unknown, atypical facial asymmetry has been reported in unaffected mothers of children with Autistic spectrum disorders [Hammond et al., 2008].

The digital nature of 3D facial data facilitates integration with other digital data. In addition to integration with other -omics technologies, there are a number of variably speculative avenues that could be pursued. Firstly, the promulgation of electronic medical records and text mining techniques, such as natural language processing, when combined with human phenotype ontology and 3D facial data, might generate novel diagnostics. Secondly, crowdsourcing may provide novel solutions. The success of foldit (foldit)and phylo (phylo) in obtaining solutions by utilizing a mass of individuals, that largely may have no formal scientific training, to solve complicated scientific problems is encouraging for further applications. For instance, analogous to sequence alignment in phylo, phenotype alignment may be possible. Although privacy concerns may preclude use of data which can visually identify an individual, the use of composite data from groups of patients, or the use of metadata may be suitable approaches. Some individuals have extraordinary facial recognition ability and they have been described as “super-recognizers” [Russell et al., 2009]. Those with inherent facial pattern recognition ability may be passively identified by crowdsourcing approaches. More generally, closing the bioinformatic gap between research and healthcare may be aided by knowledge engineering, that is, integrating knowledge into computer systems to solve complex problems typically requiring a high level of human expertise which may be facilitated by projects such as the proposed I4 health concept [Beck et al., 2012]

There are current emerging clinical applications of 3D facial analysis and a number of translational and primary research avenues of investigations which include those summarized in Table 1. Importantly, the human network remains central to fostering research and translation of research toward clinical outcomes. “The world does not connect by molecules. It connects through ideas, hopes, FACES, dreams, actions, stories and memories,” Barrie Sanford Grieff. The inherent intrigue and commonality of the human face provides a collaborative focus and a node for the development of interdisciplinary and community collaborations. Many successful examples of collaborations centerd around facial research exist and their expansion and interconnection with each other, other physical and intellectual infrastructures, and with active collaborative engagement of clinicians and researchers will be key to maximizing outcomes. Integration of global resources for human genetic variation and disease might proceed with a series of national or international hubs to hold phenotypic and genotypic data, which could be channelled to a central database [Schofield and Hancock, 2012]. From a RD health care perspective, the Orphanet approach to representation of RDs in health information systems is noted [Rath et al., 2012]. Applying coordinated approaches to RD will be a priority given the current international efforts that are supporting such collaborations, the remarkable impact that -omics technologies are making in this area, and given that rare conditions are disease models that help to understand the pathogenesis of diseases for the benefit of individuals with both common and RDs [Rath et al., 2012]

Table 1. Potential Clinical and Translational Applications of 3D Facial Analysis
  1. a

    Initially, these may occur at specialist referral centers. With analytical and hardware refinements, they may be applicable in primary care situations.

  2. NE, normal equivalent; OSA, obstructive sleep apnea.

Facial differential diagnosis and screeningaDysmorphometric facial signatures of diseases/disease groups
Facial diagnosisaDysmorphometric facial signatures of individual diseases
Craniofacial surgical planningIndividualized assessments, for example, using the NE
Postsurgical assessment and auditIndividualized assessments pre- and postprocedure
Deep phenotyping to facilitate interpretation of genomic informationPaired 3D facial-genomic (e.g., next-generation sequencing) studies
Iatrogenic morbidity reductionReplacement of ionizing radiation, for example, interposition into craniofacial surgical protocols; replacement of invasive investigations, for example, surface imaging as a proxy for polysomnography for OSA, or hematological monitoring of drug response
Cost reductionReplacement of expensive investigations in selected situations; early diagnosis
Natural history studies as a foundation for prognostication and therapeutic trialLongitudinal individualized assessments
Treatment monitoringLongitudinal individualized assessments
Drug repurposingComparative facial analysis between disorder groups
Novel diagnostics using contextual informationDigital integration with imaging and other investigations, for example, paired face-neuroimaging studies; integration with data in medical records utilizing techniques including natural language processing; or familial morphometrics
Crowd sourcingIdentify superrecognizers; phenotype alignment
Exploration of biological pathwaysComparative (between and within disorder) facial analysis; comparative (between species, e.g., mouse and human) facial analysis


  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

The high-resolution, noninvasive, nonionizing, scalable and relatively inexpensive nature of 3D facial analysis offers opportunities for novel investigations of rare and common diseases. A knowledge of evolutionary factors may be helpful for instructing and interpreting these studies which will best proceed by innovative collaborative nodal networks.


  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information
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Supporting Information

  1. Top of page
  3. Introduction
  4. Evolutionary Influences: Looking Backward
  5. Moving Forward: The evolution of Facial Analysis
  6. Conclusion
  7. References
  8. Supporting Information

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