PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bonaparte: Disaster Victim Identification System
Willem Burgers and Wim Wiegerinck
In: BNAIC 2010, 25 Oct - 26 Oct 2010, Luxemburg.


Society is increasingly aware of the possibility of a mass disaster. Recent examples are the 9/11World Trade Center attacks, the 2004 Boxing Day tsunami and various plane crashes. Recently the Netherlands Forensic Institute was confronted with the task identifying the victims of the Afriqiyah Airways flight 8U771 crash in Tripoli, Libya. In such events the recovery and identification of the remains of the victims is of great importance, both for humanitarian and legal reasons. Disaster victim identification (DVI), the identification of victims of a mass disaster, is greatly facilitated by the advent of modern DNA technology. In forensic laboratories, DNA profiles can be recorded from small samples of body remains which may otherwise be unidentifiable. The identification task is the match of the unidentified victim with a reported missing person. This is often complicated by the fact that the match has to be made in an indirect way. This is the case when there is no reliable reference material of the missing person. In such a case, DNA profiles can be taken from relatives. Since their profiles are statistically related to the profile of the missing person (first degree family members share about 50% of their DNA) an indirect match can be made. In cases with one victim, identification is a reasonable straightforward task for forensic researchers. In the case of a few victims, the puzzle to match the victims and the missing persons is often still doable by hand, using a spread sheet, or with software tools available on the internet [1]. However, large scale DVI is infeasible this way and an automated software system is indispensable for forensic institutes that need to be prepared for DVI.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
User Modelling for Computer Human Interaction
Brain Computer Interfaces
ID Code:7041
Deposited By:Bert Kappen
Deposited On:03 February 2011