Abstract
Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity. One approach to establish the identity of unidentified biological material is to predict the face from DNA, and subsequently to match against facial images. However, DNA phenotyping of the human face remains challenging. Here, another proof of concept to biometric authentication is established by using multiple face-to-DNA classifiers, each classifying given faces by a DNA-encoded aspect (sex, genomic background, individual genetic loci), or by a DNA-inferred aspect (BMI, age). Face-to-DNA classifiers on distinct DNA aspects are fused into one matching score for any given face against DNA. In a globally diverse, and subsequently in a homogeneous cohort, we demonstrate preliminary, but substantial true (83%, 80%) over false (17%, 20%) matching in verification mode. Consequences of future efforts include forensic applications, necessitating careful consideration of ethical and legal implications for privacy in genomic databases.
Highlights
Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity
The genomic principal components (PCs) were the primary molecular features of interest (Fig. 2), which were modeled in the context of sex, age, and body mass index (BMI)
Individual single nucleotide polymorphisms (SNPs) in genetic loci associated with facial variation were the main molecular features of interest and were modeled in the context of the first four genomic PCs of the EURO cohort, sex, age, and BMI
Summary
Facial recognition from DNA refers to the identification or verification of unidentified biological material against facial images with known identity. One approach to establish the identity of unidentified biological material is to predict the face from DNA, and subsequently to match against facial images. In contrast to DNA phenotyping, the idea is not to predict facial characteristics from DNA, but instead to predict DNA aspects from 3D facial shape using face-to-DNA classifiers; all information is estimated from existing 3D facial images in a database. We apply this paradigm to two cohorts illustrating different recognition challenges. We underscore the need for further validation and proper safeguards, and bring to concern the privacy and data anonymity challenges for online genomic databases in personal genomics, personalized medicine, and genomic research
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