article

Quality Measures in Biometric Systems

Authors:

Fernando Alonso-Fernandez,

Julian Fierrez,

Javier Ortega-GarciaAuthors Info & Claims

IEEE Security and Privacy, Volume 10, Issue 6

Pages 52 - 62

https://doi.org/10.1109/MSP.2011.178

Published: 01 November 2012 Publication History

Abstract

Biometric technology has been increasingly deployed in the past decade, offering greater security and convenience than traditional methods of personal recognition. Although biometric signals' quality heavily affects a biometric system's performance, prior research on evaluating quality is limited. Quality is a critical issue in security, especially in adverse scenarios involving surveillance cameras, forensics, portable devices, or remote access through the Internet. This article analyzes what factors negatively impact biometric quality, how to overcome them, and how to incorporate quality measures into biometric systems. A review of the state of the art in these matters gives an overall framework for the challenges of biometric quality.

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Nguyen KProença HAlonso-Fernandez F(2024)Deep Learning for Iris Recognition: A SurveyACM Computing Surveys10.1145/365130656:9(1-35)Online publication date: 24-Apr-2024
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Raju MFriedman LLohr DKomogortsev O(2024)Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across FrequenciesProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653353(1-7)Online publication date: 4-Jun-2024
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Shakor MSurameery N(2023)CNN-Based Transfer Learning for 3D Knuckle RecognitionAdvances in Multimedia10.1155/2023/61474222023Online publication date: 1-Jan-2023
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Quality Measures in Biometric Systems
1. Applied computing

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Published In

IEEE Security and Privacy Volume 10, Issue 6

November 2012

90 pages

ISSN:1540-7993

Issue’s Table of Contents

Publisher

IEEE Educational Activities Department

United States

Publication History

Published: 01 November 2012

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Nguyen KProença HAlonso-Fernandez F(2024)Deep Learning for Iris Recognition: A SurveyACM Computing Surveys10.1145/365130656:9(1-35)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1145/3651306
Raju MFriedman LLohr DKomogortsev O(2024)Signal vs Noise in Eye-tracking Data: Biometric Implications and Identity Information Across FrequenciesProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653353(1-7)Online publication date: 4-Jun-2024
https://dl.acm.org/doi/10.1145/3649902.3653353
Shakor MSurameery N(2023)CNN-Based Transfer Learning for 3D Knuckle RecognitionAdvances in Multimedia10.1155/2023/61474222023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/6147422
Duan XLiu HLiang J(2023)DIQA-FF:dual image quality assessment for face frontalizationMultimedia Tools and Applications10.1007/s11042-023-15084-882:25(39503-39522)Online publication date: 29-Mar-2023
https://dl.acm.org/doi/10.1007/s11042-023-15084-8
Schlett TRathgeb CHenniger OGalbally JFierrez JBusch C(2022)Face Image Quality Assessment: A Literature SurveyACM Computing Surveys10.1145/350790154:10s(1-49)Online publication date: 13-Sep-2022
https://dl.acm.org/doi/10.1145/3507901
Do T(2021)Training Neural Networks on Top of Support Vector Machine Models for Classifying Fingerprint ImagesSN Computer Science10.1007/s42979-021-00743-02:5Online publication date: 24-Jun-2021
https://dl.acm.org/doi/10.1007/s42979-021-00743-0
Tolosana RVera-Rodriguez RFierrez JOrtega-Garcia J(2020)BioTouchPass2: Touchscreen Password Biometrics Using Time-Aligned Recurrent Neural NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2020.297383215(2616-2628)Online publication date: 3-Mar-2020
https://dl.acm.org/doi/10.1109/TIFS.2020.2973832
Lafkih MMikram MGhouzali SEl Haziti M(2019)Evaluation of the Impact of Noise on Biometric Authentication SystemsProceedings of the 3rd International Conference on Advances in Artificial Intelligence10.1145/3369114.3369118(188-192)Online publication date: 26-Oct-2019
https://dl.acm.org/doi/10.1145/3369114.3369118
Krish RFierrez JRamos DAlonso-Fernandez FBigun J(2019)Improving automated latent fingerprint identification using extended minutia typesInformation Fusion10.1016/j.inffus.2018.10.00150:C(9-19)Online publication date: 1-Oct-2019
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Hernandez-Ortega JNagae SFierrez JMorales A(2019)Quality-Based Pulse Estimation from NIR Face Video with Application to Driver MonitoringPattern Recognition and Image Analysis10.1007/978-3-030-31321-0_10(108-119)Online publication date: 1-Jul-2019
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Finger-vein image quality evaluation based on the representation of grayscale and binary image

Local quality method for the iris image pattern

Biometric keypads: Improving accuracy through optimal PIN selection

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