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Latent Fingerprint Matching using Descriptor-Based Hough Transform
| Citations: | 20 - 6 self |
BibTeX
@MISC{Paulino_latentfingerprint,
author = {Alessandra A. Paulino and Jianjiang Feng and Anil K. Jain},
title = {Latent Fingerprint Matching using Descriptor-Based Hough Transform},
year = {}
}
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Abstract
Identifying suspects based on impressions of fingers lifted from crime scenes (latent prints) is extremely important to law enforcement agencies. Latents are usually partial fingerprints with small area, contain nonlinear distortion, and are usually smudgy and blurred. Due to some of these characteristics, they have a significantly smaller number of minutiae points (one of the most important features in fingerprint matching) and therefore it can be extremely difficult to automatically match latents to plain or rolled fingerprints that are stored in law enforcement databases. Our goal is to develop a latent matching algorithm that uses only minutiae information. The proposed approach consists of following three modules: (i) align two sets of minutiae by using a descriptor-based Hough Transform; (ii) establish the correspondences between minutiae; and (iii) compute a similarity score. Experimental results on NIST SD27 show that the proposed algorithm outperforms a commercial fingerprint matcher. 1.
Keyphrases
descriptor-based hough transform latent fingerprint matching law enforcement agency nist sd27 small area similarity score latent print partial fingerprint law enforcement commercial fingerprint matcher minutia information crime scene minutia point experimental result nonlinear distortion fingerprint matching important feature
