Pre-registration for Improved Latent Fingerprint Identification

Krish, Ram P.; Fiérrez, Julián; Ramos, Daniel; Ortega‐Garcia, Javier; Bigün, Josef

doi:10.1109/icpr.2014.130

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…When only partial fingerprints are available, pre-alignment of partial minutiae set and full minutiae set based on orientation fields of respective fingerprints helps in reducing the minutiae search space of full fingerprint relative to the size of partial fingerprint. Such reduction in the size of minutiae search space has been shown to improve the performance by Krish et al [27], [28], [29]. This approach has shown significant improvement in the system performance especially for poor quality latent fingerprints.…”

Section: Related Workmentioning

confidence: 98%

Improving automated latent fingerprint identification using extended minutia types

et al. 2019

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Latent fingerprints are usually processed with Automated Fingerprint Identification Systems (AFIS) by law enforcement agencies to narrow down possible suspects from a criminal database. AFIS do not commonly use all discriminatory features available in fingerprints but typically use only some types of features automatically extracted by a feature extraction algorithm. In this work, we explore ways to improve rank identification accuracies of AFIS when only a partial latent fingerprint is available. Towards solving this challenge, we propose a method that exploits extended fingerprint features (unusual/rare minutiae) not commonly considered in AFIS. This new method can be combined with any existing minutiae-based matcher. We first compute a similarity score based on least squares between latent and tenprint minutiae points, with rare minutiae features as reference points. Then the similarity score of the reference minutiae-based matcher at hand is modified based on a fitting error from the least square similarity stage. We use a realistic forensic fingerprint casework database in our experiments which contains rare minutiae features obtained from Guardia Civil, the Spanish law enforcement agency. Experiments are conducted using three * Corresponding author Email addresses: ram.krish@dcu.ie (Ram P. Krish), julian.fierrez@uam.es (Julian Fierrez), daniel.ramos@uam.es (Daniel Ramos), feralo@hh.se (Fernando Alonso-Fernandez), josef.bigun@hh.se (Josef Bigun) 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. http://creativecommons.org/licenses/by-nc-nd/4.0/ The final version is available online at: https://doi.minutiae-based matchers as a reference, namely: NIST-Bozorth3, VeriFinger-SDK and MCC-SDK. We report significant improvements in the rank identification accuracies when these minutiae matchers are augmented with our proposed algorithm based on rare minutiae features.

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Section: Related Workmentioning

confidence: 98%

Improving automated latent fingerprint identification using extended minutia types

et al. 2019

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“…(e) Latent comparison. In the absence of a robust latent minutiae extractor, published latent comparison algorithms [2], [30], [31], [32] rely on manually marked minutiae.…”

Section: Related Literaturementioning

confidence: 99%

Automated Latent Fingerprint Recognition

Cao

Jain

2019

IEEE Trans. Pattern Anal. Mach. Intell.

165

121

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Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a candidate list of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short candidate list from the reference database. Experimental results show that the rank-1 identification accuracies are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases, against a reference database of 100K rolled prints. By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6% (78.4%) on NIST SD27 and WVU latent databases, respectively.

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“…Paulino et al (2013) proposed a descriptor-based Hough transform alignment algorithm for aligning and mea-suring fingerprints similarity using minutiae and orientation fields. Also, the full fingerprint minutiae set with respect to the query latent minutiae set was reduced with the latent orientation field information (Krish et al, 2014).…”

Section: Latent Fingerprint Matchingmentioning

confidence: 99%

Latent Fingerprint Matching Using Distinctive Ridge Points

Castillo-Rosado¹,

Hernández-Palancar²

2019

Informatica

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The way that forensic examiners compare fingerprints highly differs from the behaviour of current automatic fingerprint identification algorithms. Experts usually use all the information in the fingerprint, not only minutiae, while automatic algorithms don't. Partial (especially latent) fingerprint matching algorithms still report low accuracy values in comparison to those achieved by experts. This difference is mainly due to the features used in each case. In this work, a novel approach for matching partial fingerprints is presented. We introduce a new fingerprint feature, named Distinctive Ridge Point (DRP), combined with an improved triangle-based representation which also uses minutiae. The new feature describes the neighbouring ridges of minutiae in a novel way. A modified version of a fingerprint matching algorithm presented in a previous work is used for matching two triangular representations of minutiae and DRPs. The experiments conducted on NIST27 database with a background added of 29000 tenprint impressions from NIST14 and NIST4 databases showed the benefits of this approach. The results show that using the proposal we achieved an accuracy of 70.9% in rank-1, improving in an 11% the accuracy obtained using minutiae and the reference point. This result is comparable with the best accuracy reached in the state of the art while the amount of features is reduced.

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Pre-registration for Improved Latent Fingerprint Identification

Cited by 8 publications

References 14 publications

Improving automated latent fingerprint identification using extended minutia types

Improving automated latent fingerprint identification using extended minutia types

Automated Latent Fingerprint Recognition

Latent Fingerprint Matching Using Distinctive Ridge Points

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