Lyn Haber


Technology has changed how forensic evidence is processed. Prior to the 1980’s, fingerprints were compared by humans.  Today they are routinely compared digitally, although an examiner often makes the final judgment. In more than 50% of fingerprint cases in the U.S., Automated Fingerprint Identification Systems (AFIS) search large databases in cases where police have no suspect. This entirely novel procedure has much potential for error: (1) Search systems are proprietary, it is unknown how accurately these systems locate the correct target when it is in the database;  (2) As databases increase in size, the probability of erroneous matches increases; (3) AFIS ranks and scores candidate prints according to their similarity to the inputted print, biasing examiners; (4) Because AFIS technicians must pre-process the print in order to submit it, they make decisions about ambiguous features that can lead to errors;  (5) Examiners “see” and encode different features, so that the same print, submitted by different examiners, results in different candidates; (6) When no high scoring candidates appear, examiners may re-submit the print using different features -- a high AFIS score is based on less likely features. I conclude that technology has run far ahead of solid research documenting the accuracy with which humans can employ it.


digital fingerprint comparison, AFIS, comparison biases, erroneous identifications, AFIS research

Full Text:



Ashbaugh, D.R. (1999). Quantitative-Qualitative friction ridge analysis: An introduction to basic and advanced ridgeology. Boca Raton, FL: CRC Press.

Champod, C., Lennard, C., Margot, P. & Stoilovic, M. (2004). Fingerprints and other ridge skin impressions. Boca Raton, FL: CRC Press.

Cole, S.A. (2005). More than zero: accounting for error in latent fingerprint identification. Journal of Criminal Law & Criminology, 95, pp. 985-1078.

Cole, S.A., Welling, M., Dioso-Villa, R., & Carpenter, R. (2008). Beyond the individuality of fingerprints: a measure of simulated computer latent print source attribution accuracy. Law, Probability and Risk . 7, pp.165-189.

Dror, I.E., Peron, A., Hind, S. & Charlton, D. (2005). When emotions get the better of us: the effect of contextual top-down processing on matching fingerprints. Applied Cognitive Psychology, 19(6), pp. 799-809.

Dror I.E., Mnookin J.L. (2010). The use of technology in human expert domains: challenges and risks arising from the use of automated fingerprint identification systems in forensic science. Law Probability and Risk, 9, pp. 47–67.

Dror, I.E., & Wertheim, K. (2011). Quantified assessment of AFIS contextual information on accuracy and reliability of subsequent examiner conclusions. Final Draft Technical Report, DoJ/NIJ grant #2009-DN-BX-K224.

Evett, Z.W., & Williams, R.L. (1996). Review of the 16 point fingerprint standard in England and Wales. Journal of Forensic Identification, 46, pp. 49-73.

Federal Bureau of Investigation (1988). The science of fingerprints: classification and uses. Revised Edition. Washington D.C.: United States Department of Justice.

Langenburg, G. (2004). Pilot study: a statistical analysis of the ACE-V method – analysis stage. Journal of Forensic Identification, 54, pp. 64-74.

Langenburg, G., Champod, C., & Wertheim, P. (2009). Testing for potential contextual bias effects during the verification stage of the ACE-V methodology when conducting fingerprint comparisons. Journal of Forensic Sciences, 54(3), pp. 571-582.

Mantoakis, A., Rodero, P., Lesschaeve, I., & Hastie, R. (2009). Order in choice: Effects of serial position on preferences. Psychological Science, 20, pp. 1309-1312.

Mnookin, J.L. (2004). The Achilles’ heel of fingerprints. Washington Post, p.A27.

Moses, K. (2014). Automated fingerprint identification system (AFIS). The Fingerprint Sourcebook. WA DC, National Institute of Justice, pp. 1-33.

Office of the Inspector General (2006). Oversight and Review Division, U.S. Department of Justice. ‘A Review of the FBI’s Handling of the Brandon Mayfield case’.

Olsen, R.O. (1978). Scott’s Fingerprint Mechanics. Springfield, IL: Charles C Thomas Publisher.

Stacy, R.B. (2004). Report on the erroneous fingerprint individualization bombing case. Journal of Forensic Identification, 54(6), pp. 706-718.

Ulery, B.T., Hicklin, A.R., Roberts, M.A., & Buscaglia, J. (2014). Measuring what latent fingerprint examiners consider sufficient information for individualization determinations. PLoS One, 9(11). Available at:



  • There are currently no refbacks.

Copyright (c) 2018 Journal of Media Critiques [JMC]

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.