Facial recognition from DNA using face-to-DNA classifiers

Dzemila Sero, Arslan Zaidi, Jiarui Li, Julie D. White, Tomás B. González Zarzar, Mary L. Marazita, Seth M. Weinberg, Paul Suetens, Dirk Vandermeulen, Jennifer K. Wagner, Mark D. Shriver and Peter Claes ()
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Dzemila Sero: ESAT/PSI, KU Leuven
Arslan Zaidi: Penn State University
Jiarui Li: ESAT/PSI, KU Leuven
Julie D. White: Penn State University
Tomás B. González Zarzar: Penn State University
Mary L. Marazita: University of Pittsburgh
Seth M. Weinberg: University of Pittsburgh
Paul Suetens: ESAT/PSI, KU Leuven
Dirk Vandermeulen: ESAT/PSI, KU Leuven
Jennifer K. Wagner: Geisinger Health System
Mark D. Shriver: Penn State University
Peter Claes: ESAT/PSI, KU Leuven

Nature Communications, 2019, vol. 10, issue 1, 1-12

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-10617-y

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