Facial Recognition From DNA Using Face-to-DNA Classifiers

• NCJ Number: 254032
• Journal: Nature Communications Volume: 10 Dated: 2019
• Author(s): Dzemila Sero; Arsian Zaidi; Jiarui Li; Julie D. White; Tomas B, Gonzalez-Zarzar; Mary L. Marazita; Seth M. Weinberg; Paul Suetens; Dirk Vandermeulen; Jennifer Wagner; Mark D. Shriver; Peter Claes
• Date Published: 2019
• Length: 9 pages
• Grant Number(s): 2008-DN-BX-K125
• Sponsoring Agency: National Institute of Justice (NIJ)
• Publication Format: Article
• Publication Type: Research (Applied/Empirical); Report (Study/Research); Report (Grant Sponsored); Program/Project Description
• Language: English
• Country: United States of America

Annotation

This project established another proof of concept to biometric authentication 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).

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. In the current project, face-to-DNA classifiers on distinct DNA aspects were fused into one matching score for any given face against DNA. In a globally diverse, and subsequently in a homogeneous cohort, the project demonstrated preliminary, but substantial true (83 percent, 80 percent) over false (17 percent, 20 percent) matching in verification mode. Consequences of future efforts include forensic applications, necessitating careful consideration of ethical and legal implications for privacy in genomic databases. (publisher abstract modified)