Liveness Detection Competition -- Noncontact-based Fingerprint
Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)
- URL: http://arxiv.org/abs/2310.00659v1
- Date: Sun, 1 Oct 2023 12:59:30 GMT
- Title: Liveness Detection Competition -- Noncontact-based Fingerprint
Algorithms and Systems (LivDet-2023 Noncontact Fingerprint)
- Authors: Sandip Purnapatra, Humaira Rezaie, Bhavin Jawade, Yu Liu, Yue Pan,
Luke Brosell, Mst Rumana Sumi, Lambert Igene, Alden Dimarco, Srirangaraj
Setlur, Soumyabrata Dey, Stephanie Schuckers, Marco Huber, Jan Niklas Kolf,
Meiling Fang, Naser Damer, Banafsheh Adami, Raul Chitic, Karsten Seelert,
Vishesh Mistry, Rahul Parthe, Umit Kacar
- Abstract summary: LivDet-2023 Noncontact Fingerprint is the first edition of the noncontact fingerprint-based PAD competition for algorithms and systems.
The competition serves as an important benchmark in noncontact-based fingerprint PAD.
The winning algorithm achieved an APCER of 11.35% averaged overall PAIs and a BPCER of 0.62%.
- Score: 12.05273326660349
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Liveness Detection (LivDet) is an international competition series open to
academia and industry with the objec-tive to assess and report state-of-the-art
in Presentation Attack Detection (PAD). LivDet-2023 Noncontact Fingerprint is
the first edition of the noncontact fingerprint-based PAD competition for
algorithms and systems. The competition serves as an important benchmark in
noncontact-based fingerprint PAD, offering (a) independent assessment of the
state-of-the-art in noncontact-based fingerprint PAD for algorithms and
systems, and (b) common evaluation protocol, which includes finger photos of a
variety of Presentation Attack Instruments (PAIs) and live fingers to the
biometric research community (c) provides standard algorithm and system
evaluation protocols, along with the comparative analysis of state-of-the-art
algorithms from academia and industry with both old and new android
smartphones. The winning algorithm achieved an APCER of 11.35% averaged overall
PAIs and a BPCER of 0.62%. The winning system achieved an APCER of 13.0.4%,
averaged over all PAIs tested over all the smartphones, and a BPCER of 1.68%
over all smartphones tested. Four-finger systems that make individual
finger-based PAD decisions were also tested. The dataset used for competition
will be available 1 to all researchers as per data share protocol
Related papers
- LivDet2023 -- Fingerprint Liveness Detection Competition: Advancing
Generalization [6.154783360142315]
The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD)
This edition, LivDet2023, proposed two challenges, Liveness Detection in Action and Fingerprint Representation, to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets.
arXiv Detail & Related papers (2023-09-27T11:24:01Z) - Presentation Attack Detection with Advanced CNN Models for
Noncontact-based Fingerprint Systems [5.022332693793425]
We develop a Presentation attack detection (PAD) dataset of more than 7500 four-finger images.
PAD accuracy of Attack presentation classification error rate (APCER) 0.14% and Bonafide presentation classification error rate (BPCER) 0.18%.
arXiv Detail & Related papers (2023-03-09T18:01:10Z) - Combining multiple matchers for fingerprint verification: A case study
in biosecure network of excellence [53.598636960435286]
Two reference systems for fingerprint verification have been tested together with two additional non-reference systems.
The experimental results show that the best recognition strategy involves both minutiae-based and correlation-based measurements.
arXiv Detail & Related papers (2022-12-04T19:49:05Z) - AFR-Net: Attention-Driven Fingerprint Recognition Network [47.87570819350573]
We improve initial studies on the use of vision transformers (ViT) for biometric recognition, including fingerprint recognition.
We propose a realignment strategy using local embeddings extracted from intermediate feature maps within the networks to refine the global embeddings in low certainty situations.
This strategy can be applied as a wrapper to any existing deep learning network (including attention-based, CNN-based, or both) to boost its performance.
arXiv Detail & Related papers (2022-11-25T05:10:39Z) - Reducing a complex two-sided smartwatch examination for Parkinson's
Disease to an efficient one-sided examination preserving machine learning
accuracy [63.20765930558542]
We have recorded participants performing technology-based assessments in a prospective study to research Parkinson's Disease (PD)
This study provided the largest PD sample size of two-hand synchronous smartwatch measurements.
arXiv Detail & Related papers (2022-05-11T09:12:59Z) - Mobile Behavioral Biometrics for Passive Authentication [65.94403066225384]
This work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits.
Experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases.
In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke.
arXiv Detail & Related papers (2022-03-14T17:05:59Z) - Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal
Biometric Fusion Algorithms [58.156733807470395]
This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign.
The campaign targeted the application of physical access control in a medium-size establishment with some 500 persons.
To the best of our knowledge, this is the first attempt to benchmark quality-based multimodal fusion algorithms.
arXiv Detail & Related papers (2021-11-17T13:39:48Z) - A Contactless Fingerprint Recognition System [5.565364597145569]
We propose an approach for developing a contactless fingerprint recognition system that captures finger photo from a distance.
The captured finger photos are then processed further to obtain global and local (minutiae-based) features.
The proposed system is developed using the Nvidia Jetson Nano development kit, which allows us to perform contactless fingerprint recognition in real-time.
arXiv Detail & Related papers (2021-08-20T08:21:55Z) - A Unified Model for Fingerprint Authentication and Presentation Attack
Detection [1.9703625025720706]
We reformulate the workings of a typical fingerprint recognition system.
We propose a joint model for spoof detection and matching to simultaneously perform both tasks.
This reduces the time and memory requirements of the fingerprint recognition system by 50% and 40%, respectively.
arXiv Detail & Related papers (2021-04-07T16:57:38Z) - Mobile Touchless Fingerprint Recognition: Implementation, Performance
and Usability Aspects [13.664130356074052]
This work presents an automated touchless fingerprint recognition system for smartphones.
We provide a comprehensive description of the entire recognition pipeline and discuss important requirements for a fully automated capturing system.
arXiv Detail & Related papers (2021-03-04T13:56:16Z) - Towards Palmprint Verification On Smartphones [62.279124220123286]
Studies in the past two decades have shown that palmprints have outstanding merits in uniqueness and permanence.
We built a DCNN-based palmprint verification system named DeepMPV+ for smartphones.
The efficiency and efficacy of DeepMPV+ have been corroborated by extensive experiments.
arXiv Detail & Related papers (2020-03-30T08:31:03Z)
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.