Use of Facial Recognition Software to Identify Disaster Victims With Facial Injuries
UMass Chan Affiliations
Department of Emergency MedicineDocument Type
Journal ArticlePublication Date
2017-04-10Keywords
disaster medicinedisaster victim identification
facial recognition
mass casualty incidents
Emergency Medicine
Environmental Public Health
Trauma
Metadata
Show full item recordAbstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.Source
Disaster Med Public Health Prep. 2017 Apr 10:1-5. doi: 10.1017/dmp.2016.207. Link to article on publisher's siteDOI
10.1017/dmp.2016.207Permanent Link to this Item
http://hdl.handle.net/20.500.14038/28542PubMed ID
28393744Related Resources
Link to Article in PubMedae974a485f413a2113503eed53cd6c53
10.1017/dmp.2016.207