T Model VERSION 7.0.2

 

Fingerprint Identification Based on Match Probability and Relevant Population

  

Last Update:  December 1, 2009

The Madrid Error

On March 11, 2004 a terrorist train bombing in Madrid, Spain killed 191 persons and injured numerous others.  A latent fingerprint was recovered from the scene and searched in the FBI automated fingerprint identification system database of over 500 million known fingerprints.  Subsequently, 3 expert FBI fingerprint examiners and 1 court appointed expert fingerprint examiner identified the latent fingerprint to Brandon Mayfield, a Portland, Oregon attorney.  The 4 fingerprint examiners asserted with absolute confidence that the amount of corresponding ridge formations in the two fingerprints was sufficient to individualize.  However, soon afterwards, the Spanish National Police positively identified the latent print to an Algerian, Ouhnane Daoud.  What has been called the “Madrid error” revealed that fingerprint errors occur and that innocent persons can be wrongly accused and arrested for crimes they do not commit. 

The following images show the Madrid latent print, Brandon Mayfield’s exemplar and Daoud’s exemplar (Images #1, #2 and #3).  Click here to review close up images of the Madrid error prints [22].

 

Image #1 

The Madrid Latent

 

 

 

Image #2

Brandon Mayfield's Exemplar 

 

 

 


 

Image #3

Ouhnane Daoud's Exemplar 

 

The fingerprint examiners involved in the Madrid error were accused of violating the Patriot Act, not following proper examination procedure, succumbing to the pressures of a high profile case, and so on.  However, based on the Review of the FBI's Handling of the Brandon Mayfield case by the U.S. Department of Justice Office of the Inspector General, the No. 1 major contributing cause of the error was "the unusual similarity of the prints" [77].  In other words, after searching a database of over 500 million fingerprints, the fingerprint examiners came across a portion of a fingerprint with similar looking features.  They came across a double, a twin.  They came across a look-alike

The FBI fingerprint examiners only had “professional judgment” to determine whether or not the amount of poorly corresponding ridge formations present in the two impressions was enough to individualize.  There was no other tool available that could more reliably and more accurately identify look-alikes as insufficient to individualize. 

It may be stated that the fundamental cause for the error, which is the same for every erroneous fingerprint identification ever made, was that the FBI fingerprint examiners had no tool available to them more accurate than mere "training and experience", which subsequently caused them to fail to correctly establish that the amount of poorly corresponding ridge formations present in the two impressions was insufficient to infer positive identification

The Madrid error demonstrates that look-alikes exist in fingerprints.  It shows that the “ACE-V Professional Judgment” methodology, which is the methodology most used by latent print examiners throughout the United States, is fallible.  Most importantly it demonstrates the need for a more reliable and accurate method to define how much matching fingerprint ridge detail is needed in two impressions in order to establish valid, scientific basis to infer positive identification with a reasonable degree of scientific certainty. 

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Click here to read the full review of the FBI's handling of the Brandon Mayfield case by the U.S. Department of Justice Office of the Inspector General dated March 2006.

 

Related Video

Gerry Spence discusses the erroneous fingerprint identification what could have happened to Brandon Mayfield had not the Spanish National Police correctly identified the fingerprint to Ouhnane Daoud [Click on No. 08 The Patriot Act].

 

 

 
Portland, Oregon lawyer Brandon Mayfield was arrested based on an erroneous fingerprint identification made by 4 expert fingerprint examiners.
  
 
 

 
 
The Spanish National Police identified the Madrid latent to Ouhnane Daoud. 

 

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