Facial recognition from DNA using face-to-DNA classifiers

Exploring the association between SNPs and facial morphology in a Spanish population

Understanding and predicting human external phenotypes, particularly facial shape, is of great value for individual identification. However, facial morphology is a highly complex trait. Despite its complexity, recent genome wide association studies (GWAS) have shed light on potential SNPs associated with facial features, offering a first glimpse into the likely genetic background of individual appearance. In this paper we have selected a set of 116 candidate SNPs and studied their association with facial phenotypes in a Spanish population of 412 individuals, highlighting a wide spectrum of facial morphologies worthy of investigation. We performed canonical correlation analysis (CCA) between each SNP and the observed spacial variation in facial shape, from its representation by a dense mesh of 7160 quasi-landmarks, revealing significant associations within different facial segments. In particular, ten SNPs are highlighted for their strong association within this Spanish population, some of them uncovering correlations with novel facial regions. These findings underline the importance and usefulness of conducting candidate SNP studies, not only to validate existing associations but also to unveil novel correlations within subpopulations.

Using dataflow diagrams to support research informed consent data management communications: participant perspectives

OBJECTIVES

Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

MATERIALS AND METHODS

We conducted eight semi-structured focus groups to explore whether dataflow diagrams (DFD) can complement informed consent and improve participants' understanding of data management and associated risks (N = 34 participants).

RESULTS

Our analysis found that DFDs could supplement text-based information about data management and sharing practices, such as by helping raise new questions that prompt conversation between prospective participants and members of a research team. Participants in the study emphasized the need for clear, simple, and accessible diagrams that are participant centered. Third-party access to data and sharing of sensitive health data were identified as high-risk areas requiring thorough explanation. Participants generally agreed that the design process should be led by the research team, but it should incorporate many diverse perspectives to ensure the diagram was meaningful to potential participants who are likely unfamiliar with data management. Nearly all participants rejected the idea that artificial intelligence could identify risks during the design process, but most were comfortable with it being used as a tool to format and simplify the diagram. In short, DFDs may complement standard text-based informed consent documents, but they are not a replacement.

DISCUSSION

Prospective research participants value diverse ways of learning about study risks and benefits. Our study highlights the value of incorporating information visualizations, such as DFDs, into the informed consent procedures to participate in research.

CONCLUSION

Future research should explore other ways of visualizing consent information in ways that help people to overcome digital and data literacy barriers to participating in research. However, creating a DFD requires significant time and effort from research teams. To alleviate these costs, research sponsors can support the creation of shared infrastructure, communities of practice, and incentivize researchers to develop better consent procedures.

Mapping genes for human face shape: Exploration of univariate phenotyping strategies

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Mapping genes for human face shape: exploration of univariate phenotyping strategies

Human facial shape, while strongly heritable, involves both genetic and structural complexity, necessitating precise phenotyping for accurate assessment. Common phenotyping strategies include simplifying 3D facial features into univariate traits such as anthropometric measurements (e.g., inter-landmark distances), unsupervised dimensionality reductions (e.g., principal component analysis (PCA) and auto-encoder (AE) approaches), and assessing resemblance to particular facial gestalts (e.g., syndromic facial archetypes). This study provides a comparative assessment of these strategies in genome-wide association studies (GWASs) of 3D facial shape. Specifically, we investigated inter-landmark distances, PCA and AE-derived latent dimensions, and facial resemblance to random, extreme, and syndromic gestalts within a GWAS of 8,426 individuals of recent European ancestry. Inter-landmark distances exhibit the highest SNP-based heritability as estimated via LD score regression, followed by AE dimensions. Conversely, resemblance scores to extreme and syndromic facial gestalts display the lowest heritability, in line with expectations. Notably, the aggregation of multiple GWASs on facial resemblance to random gestalts reveals the highest number of independent genetic loci. This novel, easy-to-implement phenotyping approach holds significant promise for capturing genetically relevant morphological traits derived from complex biomedical imaging datasets, and its applications extend beyond faces. Nevertheless, these different phenotyping strategies capture different genetic influences on craniofacial shape. Thus, it remains valuable to explore these strategies individually and in combination to gain a more comprehensive understanding of the genetic factors underlying craniofacial shape and related traits.

Author Summary

Advancements linking variation in the human genome to phenotypes have rapidly evolved in recent decades and have revealed that most human traits are influenced by genetic variants to at least some degree. While many traits, such as stature, are straightforward to acquire and investigate, the multivariate and multipartite nature of facial shape makes quantification more challenging. In this study, we compared the impact of different facial phenotyping approaches on gene mapping outcomes. Our findings suggest that the choice of facial phenotyping method has an impact on apparent trait heritability and the ability to detect genetic association signals. These results offer valuable insights into the importance of phenotyping in genetic investigations, especially when dealing with highly complex morphological traits.

Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review

BACKGROUND

Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation.

OBJECTIVE

This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets.

METHODS

We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success.

RESULTS

From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants.

CONCLUSIONS

On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.

Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies

Facial image-based kinship verification represents a burgeoning frontier within the realms of computer vision and biomedicine. Recent genome-wide association studies have underscored the heritability of human facial morphology, revealing its predictability based on genetic information. These revelations form a robust foundation for advancing facial image-based kinship verification. Despite strides in computer vision, there remains a discernible gap between the biomedical and computer vision domains. Notably, the absence of family photo datasets established through biological paternity testing methods poses a significant challenge. This study addresses this gap by introducing the biological kinship visualization dataset, encompassing 5773 individuals from 2412 families with biologically confirmed kinship. Our analysis delves into the distribution and influencing factors of facial similarity among parent-child pairs, probing the potential association between forensic short tandem repeat polymorphisms and facial similarity. Additionally, we have developed a machine learning model for facial image-based kinship verification, achieving an accuracy of 0.80 in the dataset. To facilitate further exploration, we have established an online tool and database, accessible at http://120.55.161.230:88/.

A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits

The phenotype-first approach (PFA) and data-driven approach (DDA) have both greatly facilitated anthropological studies and the mapping of trait-associated genes. However, the pros and cons of the two approaches are poorly understood. Here, we systematically evaluated the two approaches and analyzed 14,838 facial traits in 2,379 Han Chinese individuals. Interestingly, the PFA explained more facial variation than the DDA in the top 100 and 1,000 except in the top 10 phenotypes. Accordingly, the ratio of heterogeneous traits extracted from the PFA was much greater, while more homogenous traits were found using the DDA for different sex, age, and BMI groups. Notably, our results demonstrated that the sex factor accounted for 30% of phenotypic variation in all traits extracted. Furthermore, we linked DDA phenotypes to PFA phenotypes with explicit biological explanations. These findings provide new insights into the analysis of multidimensional phenotypes and expand the understanding of phenotyping approaches.

Machine learning applications in forensic DNA profiling: A critical review

Machine learning (ML) is a range of powerful computational algorithms capable of generating predictive models via intelligent autonomous analysis of relatively large and often unstructured data. ML has become an integral part of our daily lives with a plethora of applications, including web, business, automotive industry, clinical diagnostics, scientific research, and more recently, forensic science. In the field of forensic DNA, the manual analysis of complex data can be challenging, time-consuming, and error-prone. The integration of novel ML-based methods may aid in streamlining this process while maintaining the high accuracy and reproducibility required for forensic tools. Due to the relative novelty of such applications, the forensic community is largely unaware of ML capabilities and limitations. Furthermore, computer science and ML professionals are often unfamiliar with the forensic science field and its specific requirements. This manuscript offers a brief introduction to the capabilities of machine learning methods and their applications in the context of forensic DNA analysis and offers a critical review of the current literature in this rapidly developing field.

Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics

Sequencing of nucleic acids with nanopores has emerged as a powerful tool offering rapid readout, high accuracy, low cost and portability. This label-free method for sequencing at the single-molecule level is an achievement on its own. However, nanopores also show promise for the technologically even more challenging sequencing of polypeptides, something that could considerably benefit biological discovery, clinical diagnostics and homeland security, as current techniques lack portability and speed. Here we survey the biochemical innovations underpinning commercial and academic nanopore DNA/RNA sequencing techniques, and explore how these advances can fuel developments in future protein sequencing with nanopores.

Sentinel cells programmed to respond to environmental DNA including human sequences

Monitoring environmental DNA can track the presence of organisms, from viruses to animals, but requires continuous sampling of transient sequences from a complex milieu. Here we designed living sentinels using Bacillus subtilis to report the uptake of a DNA sequence after matching it to a preencoded target. Overexpression of ComK increased DNA uptake 3,000-fold, allowing for femtomolar detection in samples dominated by background DNA. This capability was demonstrated using human sequences containing single-nucleotide polymorphisms (SNPs) associated with facial features. Sequences were recorded with high efficiency and were protected from nucleases for weeks. The SNP could be determined by sequencing or in vivo using CRISPR interference to turn on reporter expression in response to a specific base. Multiple SNPs were recorded by one cell or through a consortium in which each member recorded a different sequence. Sentinel cells could surveil for specific sequences over long periods of time for applications spanning forensics, ecology and epidemiology.

Curious about race: Generous methods and modes of knowing in practice

What is race? And how does it figure in different scientific practices? To answer these questions, I suggest that we need to know race differently. Rather than defining race or looking for one conclusive answer to what it is, I propose methods that are open-ended, that allow us to follow race around, while remaining curious as to what it is. I suggest that we pursue generous methods. Drawing on empirical examples of forensic identification technologies, I argue that the slipperiness of race-the way race and its politics inexorably shift and change-cannot be fully grasped as an 'object multiple'. Race, I show, is not race: The same word refers to different phenomena. To grasp this, I introduce the notion of the affinity concept. Drawing on the history of race, along with contemporary work in forensic genetics, the affinity concept helps us articulate how race indexes three different scientific realities: race as object, race as method, and race as theory. These three different, yet interconnected realities, contribute to race's slipperiness as well as its virulence.

A genome-wide association scan reveals novel loci for facial traits of Koreans

DNA-based prediction of externally visible characteristics (EVC) with SNPs is one of the research areas of interest in the forensic field. Based on a previous study performing GWAS on facial traits in a Korean population, herein, we present results stemming from GWA analysis with KoreanChip and novel genetic loci satisfying genome-wide significant level. We discovered a total of 20 signals and 12 loci were found to have novel associations with facial traits, including six loci located in intergenic regions and six loci located at UBE2O, HECTD2, CCDC108, TPK1, FCN2, and FRMPD1. Additionally, we performed a polygenic score analysis for 33 distance-related traits in facial phenotyping and determined genetic relationships between facial traits and SNPs using the GCTA program. The results of the current study offer an understanding of how facial morphology is influenced by complex genetic structures and provide insights into forensic investigation and population genetics.

Advancement in Human Face Prediction Using DNA

The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.

Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology

Variations in the form of the human face, which plays a role in our individual identities and societal interactions, have fascinated scientists and artists alike. Here, we review our current understanding of the genetics underlying variation in craniofacial morphology and disease-associated dysmorphology, synthesizing decades of progress on Mendelian syndromes in addition to more recent results from genome-wide association studies of human facial shape and disease risk. We also discuss the various approaches used to phenotype and quantify facial shape, which are of particular importance due to the complex, multipartite nature of the craniofacial form. We close by discussing how experimental studies have contributed and will further contribute to our understanding of human genetic variation and then proposing future directions and applications for the field.

Sociotechnical safeguards for genomic data privacy

Recent developments in a variety of sectors, including health care, research and the direct-to-consumer industry, have led to a dramatic increase in the amount of genomic data that are collected, used and shared. This state of affairs raises new and challenging concerns for personal privacy, both legally and technically. This Review appraises existing and emerging threats to genomic data privacy and discusses how well current legal frameworks and technical safeguards mitigate these concerns. It concludes with a discussion of remaining and emerging challenges and illustrates possible solutions that can balance protecting privacy and realizing the benefits that result from the sharing of genetic information.

Could routine forensic STR genotyping data leak personal phenotypic information?

The application of forensic genetic markers must comply with privacy rights and legal policies on a premise that the markers do not expose phenotypic information. The most widely-used short tandem repeats (STRs) are generally viewed as 'junk' DNA because most STRs are located in non-coding regions and therefore refrain from leaking phenotypic traits. But with a deepening understanding of phenotypes and underlying genetic structure, whether STRs could potentially reflect any phenotypic information may need re-examining. Therefore, we performed the following analyses. First, we analyzed the association between 15 STRs and three facial characteristics (single or double eyelid, with or without epicanthus, unattached or attached earlobe) on 721 unrelated Han Chinese individuals. Then, we collected 27199 individuals' STRs and geographic data from the literature to investigate the association between STRs and bio-geographic information, and predict geographic information by STRs on additional 1993 unrelated individuals. We found that there was scarcely any association between STRs with studied facial characteristics. Although allele19 in D2S1338 and allele 18 in FGA (P = 0.0032, P = 0.0030, respectively after Bonferroni correction) showed statistical significance, the prediction effectiveness was very low. For the STRs and bio-geographic information, the principal component analysis showed the first three components could explain 87.7% of the variance, but the prediction accuracy only reached 25.2%. We demonstrated that the forensic phenotypes are usually complex traits, it is hardly possible to uncover phenotypic information by testing only dozens of STR loci.

Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach

Face recognition is a widely accepted biometric identifier, as the face contains a lot of information about the identity of a person. The goal of this study is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background) that are extracted from unidentified genetic material. We introduce a triplet loss metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. The metric learner is trained for multiple facial segments to allow a global-to-local part-based analysis of the face. To learn directly from 3D mesh data, spiral convolutions are used along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The capacity of the model for establishing identity from facial shape against a list of probe demographics is evaluated by enrolling the embeddings for all properties into a support vector machine classifier or regressor and then combining them using a naive Bayes score fuser. Results obtained by a 10-fold cross-validation for biometric verification and identification show that part-based learning significantly improves the systems performance for both encoding with our geometric metric learner or with principal component analysis.

Multi-scale Chimerism: An experimental window on the algorithms of anatomical control

Despite the immense progress in genetics and cell biology, major knowledge gaps remain with respect to prediction and control of the global morphologies that will result from the cooperation of cells with known genomes. The understanding of cooperativity, competition, and synergy across diverse biological scales has been obscured by a focus on standard model systems that exhibit invariant species-specific anatomies. Morphogenesis of chimeric biological material is an especially instructive window on the control of biological growth and form because it emphasizes the need for prediction without reliance on familiar, standard outcomes. Here, we review an important and fascinating body of data from experiments utilizing DNA transfer, cell transplantation, organ grafting, and parabiosis. We suggest that these are all instances (at different levels of organization) of one general phenomenon: chimerism. Multi-scale chimeras are a powerful conceptual and experimental tool with which to probe the mapping between properties of components and large-scale anatomy: the laws of morphogenesis. The existing data and future advances in this field will impact not only the understanding of cooperation and the evolution of body forms, but also the design of strategies for system-level outcomes in regenerative medicine and swarm robotics.

Digital DNA lifecycle security and privacy: an overview

DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people's current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term 'digital DNA life cycle' and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.

Using game theory to thwart multistage privacy intrusions when sharing data

Person-specific biomedical data are now widely collected, but its sharing raises privacy concerns, specifically about the re-identification of seemingly anonymous records. Formal re-identification risk assessment frameworks can inform decisions about whether and how to share data; current techniques, however, focus on scenarios where the data recipients use only one resource for re-identification purposes. This is a concern because recent attacks show that adversaries can access multiple resources, combining them in a stage-wise manner, to enhance the chance of an attack’s success. In this work, we represent a re-identification game using a two-player Stackelberg game of perfect information, which can be applied to assess risk, and suggest an optimal data sharing strategy based on a privacy-utility tradeoff. We report on experiments with large-scale genomic datasets to show that, using game theoretic models accounting for adversarial capabilities to launch multistage attacks, most data can be effectively shared with low re-identification risk.

Toward a Framework for Assessing Privacy Risks in Multi-Omic Research and Databases

While the accumulation and increased circulation of genomic data have captured much attention over the past decade, privacy risks raised by the diversification and integration of omics have been largely overlooked. In this paper, we propose the outline of a framework for assessing privacy risks in multi-omic research and databases. Following a comparison of privacy risks associated with genomic and epigenomic data, we dissect ten privacy risk-impacting omic data properties that affect either the risk of re-identification of research participants, or the sensitivity of the information potentially conveyed by biological data. We then propose a three-step approach for the assessment of privacy risks in the multi-omic era. Thus, we lay grounds for a data property-based, 'pan-omic' approach that moves away from genetic exceptionalism. We conclude by inviting our peers to refine these theoretical foundations, put them to the test in their respective fields, and translate our approach into practical guidance.

A survey of U.S. public perspectives on facial recognition technology and facial imaging data practices in health and research contexts

Facial imaging and facial recognition technologies, now common in our daily lives, also are increasingly incorporated into health care processes, enabling touch-free appointment check-in, matching patients accurately, and assisting with the diagnosis of certain medical conditions. The use, sharing, and storage of facial data is expected to expand in coming years, yet little is documented about the perspectives of patients and participants regarding these uses. We developed a pair of surveys to gather public perspectives on uses of facial images and facial recognition technologies in healthcare and in health-related research in the United States. We used Qualtrics Panels to collect responses from general public respondents using two complementary and overlapping survey instruments; one focused on six types of biometrics (including facial images and DNA) and their uses in a wide range of societal contexts (including healthcare and research) and the other focused on facial imaging, facial recognition technology, and related data practices in health and research contexts specifically. We collected responses from a diverse group of 4,048 adults in the United States (2,038 and 2,010, from each survey respectively). A majority of respondents (55.5%) indicated they were equally worried about the privacy of medical records, DNA, and facial images collected for precision health research. A vignette was used to gauge willingness to participate in a hypothetical precision health study, with respondents split as willing to (39.6%), unwilling to (30.1%), and unsure about (30.3%) participating. Nearly one-quarter of respondents (24.8%) reported they would prefer to opt out of the DNA component of a study, and 22.0% reported they would prefer to opt out of both the DNA and facial imaging component of the study. Few indicated willingness to pay a fee to opt-out of the collection of their research data. Finally, respondents were offered options for ideal governance design of their data, as "open science"; "gated science"; and "closed science." No option elicited a majority response. Our findings indicate that while a majority of research participants might be comfortable with facial images and facial recognition technologies in healthcare and health-related research, a significant fraction expressed concern for the privacy of their own face-based data, similar to the privacy concerns of DNA data and medical records. A nuanced approach to uses of face-based data in healthcare and health-related research is needed, taking into consideration storage protection plans and the contexts of use.

3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies

The analysis of contemporary genomic data typically operates on one-dimensional phenotypic measurements (e.g. standing height). Here we report on a data-driven, family-informed strategy to facial phenotyping that searches for biologically relevant traits and reduces multivariate 3D facial shape variability into amendable univariate measurements, while preserving its structurally complex nature. We performed a biometric identification of siblings in a sample of 424 children, defining 1,048 sib-shared facial traits. Subsequent quantification and analyses in an independent European cohort (n = 8,246) demonstrated significant heritability for a subset of traits (0.17-0.53) and highlighted 218 genome-wide significant loci (38 also study-wide) associated with facial variation shared by siblings. These loci showed preferential enrichment for active chromatin marks in cranial neural crest cells and embryonic craniofacial tissues and several regions harbor putative craniofacial genes, thereby enhancing our knowledge on the genetic architecture of normal-range facial variation.

Insights into the genetic architecture of the human face

The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study meta-analysis of 8,246 European individuals, we identified 203 genome-wide-significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses indicate that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions.

[Forensic phenotyping: How far along?]

The prediction of a person's aspect from analysis of an anonymous DNA sample has made significant progress in the last decade. Pigmentation (eyes, hair and, more recently, skin colour) can now be determined with good accuracy; face shape is still not amenable to prediction (except, in general lines, from ancestry). Age can apparently also be determined from methylation profiles. Police forces are, understandably, very interested in this technology, with a tendency to over-estimate its accuracy. Legislation varies greatly, with some nations opting for complete prohibition (Germany) and others allowing wide application of the approach (United Kingdom).

Facial-recognition algorithms: A literature review

The face is an important part of the human body, distinguishing individuals in large groups of people. Thus, because of its universality and uniqueness, it has become the most widely used and accepted biometric method. The domain of face recognition has gained the attention of many scientists, and hence it has become a standard benchmark in the area of human recognition. It has turned out to be the most deeply studied area in computer vision for more than four decades. It has a wide array of applications, including security monitoring, automated surveillance systems, victim and missing-person identification and so on. This review presents the broad range of methods used for face recognition and attempts to discuss their advantages and disadvantages. Initially, we present the basics of face-recognition technology, its standard workflow, background and problems, and the potential applications. Then, face-recognition methods with their advantages and limitations are discussed. The concluding section presents the possibilities and future implications for further advancing the field.