Problems With Fingerprint Identification

For a long time, we have been told that fingerprint identification is one of the best forms of identification. It is common to see the advantages of fingerprint identification plastered around the internet, but hard to see the drawbacks.

Fingerprint identification is a form of biometrics, and like every biometrics method in existence, it is not fool-proof.

Generally, biometric identification occurs when the physical attributes of a person are used in identifying the said person. It can be through voice, eye features, fingerprint identification, and so on.

The physical attributes of fingerprint identification differ from one person to another. Usually, this form of identification is fast compared to others.

Not minding that there are benefits of fingerprint identification, one can still come across its drawbacks. There are some flaws of fingerprint identification that we’ll review here.

Studies have tested groups of fingerprint examiners to see if they all make the same identification and the correct identification. The test was set up so that the examiners looked at two pairs of fingerprints and had to decide whether the fingerprints were a match. Five examiners were tested, who on average had 17 years of experience. The examiners could not agree and their decisions did not match each other. The bottom line is if you have multiple examiners examine a set of fingerprints, they may not form a consensus on whether or not the fingerprints match those of the suspect.

This may be due to context bias, which means that an examiner may consider other information told to him or her apart from the fingerprint evidence itself. For instance, if the examiner is told a lot of background details, the examiner may believe the suspect is guilty and that believe could influence the result of the examination.

One way law enforcement may try to account for potential context bias is to take the element of human error out of the equation. Enter: Automated Fingerprint Identification System or AFIS. But even this method of fingerprint analysis is not perfect. Some studies show an error rate of over 27% using the AFIS method.

There are also problems with visualizing latent prints. This may depend on the type of material the fingerprint is on. For instance, if the fingerprint is found on a metal, such as the casing of a bullet, human salt and sweat could compromise or enhance the print or render a partial print.

There is a lot of speculation as to how long a fingerprint lasts. That too will depend on the material the print is on (such as a corroded metal). The problem is the longer a fingerprint lasts, the more susceptible it is to external manipulations and also the more people would have opportunity to taint or contaminate the print.

Another problem is there is no standard used worldwide on the quantity and quality of the number of comparison points on a pair of fingerprints that is necessary to make a positive identification. This means that while one examiner may look at a pair of fingerprints and determine there are 4 points on the prints that match, another examiner may determine that just 4 points that match is insufficient to make a positive identification.

The final concern with fingerprint identification is less to do with analysis and more to do with the ease with which fingerprints can be stolen and hacked.

If a person touches a surface, the fingerprint of the person may easily be extracted. This is one reason fingerprints have been easily planted in the scenes of a crime. People with grievances against others can decide to extract their fingerprints and plant them in the scenes of crime. This is a major flaw of fingerprint identification that has been noticed for years.

Hackers can recreate the biometric attributes of those that they want to victimize. Some popular hackers have successfully recreated biometric information of others, and an example is Jan Krissler. He was able to hack into the biometric identification of the iPhone and access it effortlessly. It was easy for him to recreate the fingerprint of Ursula von der Leyen, then German Minister of Defense. All he had to do was capture the fingerprint of the German minister when he was on TV.

Another example is the Chaos Computer Club. The team members decided to beat the fingerprint feature of a number of iPhones by creating molds of fingers.

If you have been falsely accused of child abuse and fingerprint analysis was used to support the case against you, all is not lost. A strong defense can still be mounted by attacking the fingerprint evidence against you.