Quality Measures in Biometric Systems
Fernando Alonso-Fernandez,Julian Fierrez,Javier Ortega-Garcia +2 more
- Vol. 10, Iss: 6, pp 52-62
TLDR
What factors negatively impact biometric quality, how to overcome them, and how to incorporate quality measures into biometric systems are analyzed.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.read more
Citations
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Journal ArticleDOI
Biometric quality: a review of fingerprint, iris, and face
TL;DR: The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.
Journal ArticleDOI
Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation
TL;DR: It is observed that soft biometrics is a valuable complement to the face modality in unconstrained scenarios, with relative improvements up to 40%/15% in the verification performance when using manual/automatic soft biometricrics estimation.
Proceedings ArticleDOI
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
TL;DR: A Quality Assessment approach for face recognition based on deep learning that employs an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information and shows that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development.
Journal ArticleDOI
Multiple classifiers in biometrics. Part 2: Trends and challenges
TL;DR: Recent trends and developments in MCS coming from multimodal biometrics that incorporate context information in an adaptive way are presented and methods are described in a general way so they can be applied to other information fusion problems as well.
Journal ArticleDOI
View Transformation Model Incorporating Quality Measures for Cross-View Gait Recognition
TL;DR: A VTM incorporating a score normalization framework with quality measures that encode the degree of the bias is proposed, and the experimental results show that incorporating the quality measures contributes to accuracy improvement in many cross-view settings.
References
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Journal ArticleDOI
Performance of Biometric Quality Measures
Patrick J. Grother,Elham Tabassi +1 more
TL;DR: This work documents methods for the quantitative evaluation of systems that produce a scalar summary of a biometric sample's quality, motivated by a need to test claims that quality measures are predictive of matching performance.
Journal ArticleDOI
A Comparative Study of Fingerprint Image-Quality Estimation Methods
Fernando Alonso-Fernandez,Julian Fierrez,Javier Ortega-Garcia,Joaquin Gonzalez-Rodriguez,H. Fronthaler,K. Kollreider,Josef Bigun +6 more
TL;DR: In this work, existing approaches for fingerprint image-quality estimation are reviewed, including the rationale behind the published measures and visual examples showing their behavior under different quality conditions, and a selection offinger image- quality estimation algorithms are tested.
Journal ArticleDOI
The Multiscenario Multienvironment BioSecure Multimodal Database (BMDB)
Javier Ortega-Garcia,Julian Fierrez,Fernando Alonso-Fernandez,Javier Galbally,M.R. Freire,Joaquin Gonzalez-Rodriguez,Carmen García-Mateo,J.L. Alba-Castro,Elisardo González-Agulla,Enrique Otero-Muras,Sonia Garcia-Salicetti,Lorene Allano,Bao Ly-Van,Bernadette Dorizzi,Josef Kittler,Thirimachos Bourlai,Norman Poh,Farzin Deravi,Ming Ng,Michael Fairhurst,Jean Hennebert,Andreas Humm,Massimo Tistarelli,Linda Brodo,Jonas Richiardi,Andrezj Drygajlo,Harald Ganster,Federico M. Sukno,Sri-Kaushik Pavani,Alejandro F. Frangi,Lale Akarun,Arman Savran +31 more
TL;DR: A new multimodal biometric database designed and acquired within the framework of the European BioSecure Network of Excellence is presented, comprised of more than 600 individuals acquired simultaneously in three scenarios: over the Internet, in an office environment with desktop PC, and in indoor/outdoor environments with mobile portable hardware.
Biometrics of Next Generation: An Overview
Anil K. Jain,Ajay Kumar +1 more
TL;DR: The next generation biometric technology must overcome many hurdles and challenges to improve the recognition accuracy, including ability to handle poor quality and incomplete data, achieve scalability to accommodate hundreds of millions of users, ensure interoperability, and protect user privacy while reducing system cost and enhancing system integrity.
Proceedings ArticleDOI
Face recognition: Some challenges in forensics
TL;DR: Improvements in forensic face recognition through research in facial aging, facial marks, forensic sketch recognition, face recognition in video, near-infrared face recognition, and use of soft biometrics will be discussed.