Deep Slap Fingerprint Segmentation for Juveniles and Adults
- URL: http://arxiv.org/abs/2110.04067v1
- Date: Wed, 6 Oct 2021 04:48:23 GMT
- Title: Deep Slap Fingerprint Segmentation for Juveniles and Adults
- Authors: M. G. Sarwar Murshed, Robert Kline, Keivan Bahmani, Faraz Hussain,
Stephanie Schuckers
- Abstract summary: We develop a human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples and 6706 are samples drawn from children from ages 4 to 12.
We evaluate the matching performance of the NFSEG, a slap fingerprint segmentation system developed by NIST, on slaps from adults and juvenile subjects.
- Score: 1.5411929268269822
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Many fingerprint recognition systems capture four fingerprints in one image.
In such systems, the fingerprint processing pipeline must first segment each
four-fingerprint slap into individual fingerprints. Note that most of the
current fingerprint segmentation algorithms have been designed and evaluated
using only adult fingerprint datasets. In this work, we have developed a
human-annotated in-house dataset of 15790 slaps of which 9084 are adult samples
and 6706 are samples drawn from children from ages 4 to 12. Subsequently, the
dataset is used to evaluate the matching performance of the NFSEG, a slap
fingerprint segmentation system developed by NIST, on slaps from adults and
juvenile subjects. Our results reveal the lower performance of NFSEG on slaps
from juvenile subjects. Finally, we utilized our novel dataset to develop the
Mask-RCNN based Clarkson Fingerprint Segmentation (CFSEG). Our matching results
using the Verifinger fingerprint matcher indicate that CFSEG outperforms NFSEG
for both adults and juvenile slaps. The CFSEG model is publicly available at
\url{https://github.com/keivanB/Clarkson_Finger_Segment}
Related papers
- FingerVeinSyn-5M: A Million-Scale Dataset and Benchmark for Finger Vein Recognition [72.52509163913626]
We introduce FVeinSyn, a synthetic generator capable of producing diverse finger vein patterns with rich intra-class variations.<n>Using FVeinSyn, we created FingerVeinSyn-5M -- the largest available finger vein dataset.<n>Models pretrained on FingerVeinSyn-5M achieve an average 53.91% performance gain across multiple benchmarks.
arXiv Detail & Related papers (2025-06-04T07:27:33Z) - Scalable Fingerprinting of Large Language Models [46.26999419117367]
We introduce a new method, dubbed Perinucleus sampling, to generate scalable, persistent, and harmless fingerprints.
We demonstrate that this scheme can add 24,576 fingerprints to a Llama-3.1-8B model without degrading the model's utility.
arXiv Detail & Related papers (2025-02-11T18:43:07Z) - A Universal Latent Fingerprint Enhancer Using Transformers [47.87570819350573]
This study aims to develop a fast method, which we call ULPrint, to enhance various latent fingerprint types.
In closed-set identification accuracy experiments, the enhanced image was able to improve the performance of the MSU-AFIS from 61.56% to 75.19%.
arXiv Detail & Related papers (2023-05-31T23:01:11Z) - Deep Age-Invariant Fingerprint Segmentation System [2.654502128955621]
Fingerprint-based identification systems achieve higher accuracy when a slap containing multiple fingerprints of a subject is used instead of a single fingerprint.
segmenting or auto-localizing all fingerprints in a slap image is a challenging task due to the different orientations of fingerprints, noisy backgrounds, and the smaller size of fingertip components.
We introduce a method to generate arbitrary angled bounding boxes using a deep learning-based algorithm that precisely localizes and labels fingerprints from both axis-aligned and over-rotated slap images.
arXiv Detail & Related papers (2023-03-06T18:21:16Z) - A review of schemes for fingerprint image quality computation [66.32254395574994]
This paper reviews existing approaches for fingerprint image quality computation.
We also implement, test and compare a selection of them using the MCYT database including 9000 fingerprint images.
arXiv Detail & Related papers (2022-07-12T10:34:03Z) - Synthesis and Reconstruction of Fingerprints using Generative
Adversarial Networks [6.700873164609009]
We propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture.
We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity.
The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction.
arXiv Detail & Related papers (2022-01-17T00:18:00Z) - PrintsGAN: Synthetic Fingerprint Generator [39.804969475699345]
PrintsGAN is a synthetic fingerprint generator capable of generating unique fingerprints along with multiple impressions for a given fingerprint.
We show the utility of the PrintsGAN generated by training a deep network to extract a fixed-length embedding from a fingerprint.
arXiv Detail & Related papers (2022-01-10T22:25:10Z) - Sensor-invariant Fingerprint ROI Segmentation Using Recurrent
Adversarial Learning [5.740220134446289]
We propose a recurrent adversarial learning based feature alignment network that helps the fingerprint roi segmentation model to learn sensor-invariant features.
Experiments on publicly available FVC databases demonstrate the efficacy of the proposed work.
arXiv Detail & Related papers (2021-07-03T07:16:39Z) - High Fidelity Fingerprint Generation: Quality, Uniqueness, and Privacy [1.3911984813936993]
We utilize progressive growth-based Generative Adversarial Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG)
We demonstrate that the CFG is capable of generating realistic, high fidelity, $512times512$ pixels, full, plain impression fingerprints.
Our results suggest that the fingerprints generated by the CFG are unique, diverse, and resemble the training dataset in terms of minutiae configuration and quality.
arXiv Detail & Related papers (2021-05-21T15:18:28Z) - ProxyFAUG: Proximity-based Fingerprint Augmentation [81.15016852963676]
ProxyFAUG is a rule-based, proximity-based method of fingerprint augmentation.
The best performing positioning method on this dataset is improved by 40% in terms of median error and 6% in terms of mean error, with the use of the augmented dataset.
arXiv Detail & Related papers (2021-02-04T15:59:30Z) - Responsible Disclosure of Generative Models Using Scalable
Fingerprinting [70.81987741132451]
Deep generative models have achieved a qualitatively new level of performance.
There are concerns on how this technology can be misused to spoof sensors, generate deep fakes, and enable misinformation at scale.
Our work enables a responsible disclosure of such state-of-the-art generative models, that allows researchers and companies to fingerprint their models.
arXiv Detail & Related papers (2020-12-16T03:51:54Z) - Latent Fingerprint Registration via Matching Densely Sampled Points [100.53031290339483]
Existing latent fingerprint registration approaches are mainly based on establishing correspondences between minutiae.
We propose a non-minutia latent fingerprint registration method which estimates the spatial transformation between a pair of fingerprints.
The proposed method achieves the state-of-the-art registration performance, especially under challenging conditions.
arXiv Detail & Related papers (2020-05-12T15:51:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.