Add like
Add dislike
Add to saved papers

Supporting fingerprint identification assessments using a skin stretch model - A preliminary study.

To support fingerprint expert opinion, this research proposes an approach that combines subjective human analysis (as currently applied by fingerprint practitioners) with a statistical test of the result. This approach relies on the hypothesis that there are limits to the distortion caused by skin stretch. Such limits can be modelled by applying a multivariate normal probability density function to the distances and angle formed by a marked ridge characteristic and the two closest neighbouring minutiae. This study presents a model tested on 5 donors in total. The "expected range" of distortion in a within-source comparison using 10 minutiae was determined and compared to between-source comparisons. The expected range of log probability densities for within-source comparisons marked with 10 minutiae was determined to be from -33.4 to -60.0, with all between-source data falling outside this range, between -83 and -305. These results suggest that the proposed generated metric could be a powerful tool for the assessment of fingerprint expert opinion in operational casework.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

Related Resources

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app