title: Use of Facial Recognition Software to Identify Disaster Victims With Facial Injuries.
abstract: After large-scale disasters, victim identification frequently presents a challenge and a priority for responders attempting to reunite families and ensure proper identification of deceased persons. The purpose of this investigation was to determine whether currently commercially available facial recognition software can successfully identify disaster victims with facial injuries.
  
  
  
  Photos of 106 people were taken before and after application of moulage designed to simulate traumatic facial injuries. These photos as well as photos from volunteers' personal photo collections were analyzed by using facial recognition software to determine whether this technology could accurately identify a person with facial injuries.
  
  
  
  The study results suggest that a responder could expect to get a correct match between submitted photos and photos of injured patients between 39% and 45% of the time and a much higher percentage of correct returns if submitted photos were of optimal quality with percentages correct exceeding 90% in most situations.
  
  
  
  The present results suggest that the use of this software would provide significant benefit to responders. Although a correct result was returned only 40% of the time, this would still likely represent a benefit for a responder trying to identify hundreds or thousands of victims. (Disaster Med Public Health Preparedness. 2017;11:568-572).
authors.affiliation: 1University of Massachusetts Medical School/University of Massachusetts Memorial Medical Center,Worcester,Massachusetts.
authors.forename: John
authors.initials: J
authors.lastname: Broach
authors.affiliation: 2Department of Emergency Medicine,Lowell General Hospital,Lowell,Massachusetts.
authors.forename: Rothsovann
authors.initials: R
authors.lastname: Yong
authors.affiliation: 3Urgent Care,Harrington HealthCare System,Southbridge,Massachusetts.
authors.forename: Mary-Elise
authors.initials: ME
authors.lastname: Manuell
authors.affiliation: 1University of Massachusetts Medical School/University of Massachusetts Memorial Medical Center,Worcester,Massachusetts.
authors.forename: Constance
authors.initials: C
authors.lastname: Nichols
mesh_terms: D064891:Disaster Victims / Q000145:classification* / Q000706:statistics & numerical data; D005151:Facial Injuries / Q000150:complications*; D000066499:Facial Recognition; D006801:Humans; D054527:Mass Casualty Incidents / Q000706:statistics & numerical data; D012984:Software / Q000592:standards*
keywords: disaster medicine; disaster victim identification; facial recognition; mass casualty incidents