Government Technology

Automated Fingerprint Identification Improving


Automated Fingerprint Identification
Automated Fingerprint Identification

April 23, 2009 By

Photo: Determining the identity of the owners of latent prints is often key to crime scene research, such as the owner of this latent printed pulled from a fingernail file. A NIST test shows that emerging software that automates the extraction of distinguishing features from latent prints is more advanced than expected. (Credit: NIST)

As anyone who watches CSI or other TV crime show knows, when fingerprints are found at the scene of a crime, computer searchers are done to see if the fingerprint already exists in criminal databases. What police know, however, is that so-called "latents" as fingerprints left behind are called, what they get is often only part of the finger -- sometimes just a few ridges. Or the latent might sometimes be left on textured materials, adding even more challenges.

To identify the owner, a fingerprint examiner must first carefully mark the distinguishing features of the full or partial print, beginning with the positions where ridges end or branch. Then the latent is entered into a counter-terrorist or law enforcement identification system such as the Federal Bureau of Investigation's Integrated Automated Fingerprint Identification System (IAFIS). The FBI's system compares latents against the 55 million sets of ten-print cards taken at arrest.

The IAFIS system was a significant advance, but it did require some skill in correctly marking distinguishing points. For this reason, new technology is emerging that automates this part of the process. It is called Automatic Feature Extraction and Matching (AFEM) and recently National Institute of Standards and Technology (NIST) biometric researchers have begun assessing prototypes that eight vendors are developing.

To conduct a real world test, the researchers used a data set of 835 latent prints and 100,000 fingerprints taken from actual case examinations.

The AFEM software extracted the distinguishing features from the latent prints, then compared them against 100,000 fingerprints. For each print the software provided a list of 50 candidates that the fingerprint specialists compared by hand. Most identities were found within the top 10.

According to a NIST report, in order of performance, the most accurate prototypes were furnished by NEC Corp., Cogent Inc., SPEX Forensics, Inc., Motorola, Inc. and L1 Identity Solutions. Results ranged from nearly 100 percent for the most accurate product to around 80 percent for the last three listed.

However, the technology is not yet ready for prime time according to a statement released by NIST. "While the testing has demonstrated accuracy beyond pre-test expectations, the potential of the technology remains undefined and further testing is required," said computer scientist Patrick Grother in the statement. "In the future we will look at lower quality latent images to understand the technology's limitations and we will support development of a standardized feature set that extends the one currently used by examiners for searches."

The report, An Evaluation of Automated Latent Fingerprint Identification Technologies, is available at http://fingerprint.nist.gov/latent/NISTIR_7577_ELFT_PhaseII.pdf

 


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