"NIST recently released two resources aimed at helping forensic fingerprint examiners do their jobs better. One is a fully annotated version of NIST's Special Database 302, a collection of roughly 10,000 latent fingerprint images. The other is what NIST calls OpenLQM, newly created open-source software that helps assess the quality of latent fingerprints and sort them according to how much useful detail they contain."
"Fingerprint analysis is one of those forensic tools that many people assume was perfected long ago. In reality, examiners often work with partial, smudged or otherwise imperfect prints recovered from real-world objects. Training people to evaluate those prints well takes experience, repetition and good examples."
"NIST says the newly completed dataset will help train both human examiners and machine learning algorithms to distinguish important features and weigh their value as evidence."
"The most vivid part of the NIST fingerprint accuracy project is how ordinary the source material really was. As NIST computer scientist Greg Fiumara explained, 'The prints are from people we recruited to come in and do things like write a note, pick up a circuit.'"
The National Institute of Standards and Technology has released two resources to improve forensic fingerprint examination. One resource is a fully annotated database of approximately 10,000 latent fingerprint images. The other is OpenLQM, open-source software designed to assess the quality of latent fingerprints. These tools aim to assist both human examiners and machine learning algorithms in evaluating partial or imperfect prints, enhancing training and accuracy in criminal investigations.
Read at Nextgov.com
Unable to calculate read time
Collection
[
|
...
]