NDLI: Literature review of fingerprint quality assessment and its evaluation

Content Provider	IET Digital Library
Author	Yao, Zhigang Bars, Jean Marie Le Charrier, Christophe Rosenberger, Christophe
Abstract	Fingerprint quality assessment (FQA) has been a challenging issue due to a variety of noisy information contained in the samples, such as physical defect and distortions caused by sensing devices. Existing studies have made efforts to find out more suitable techniques for assessing fingerprint quality but it is difficult to achieve a common solution because of, for example, different image settings. This study is two-fold, related to FQA, including a literature review of the prior work in assessing fingerprint image quality and the associated evaluation approaches. First, the authors categorised some representative studies proposed in last few decades to show how this problem has been solved so far. Second, this study gives a brief introduction of the associated evaluation approaches, and then contributes an extended evaluation framework based on the enrolment selection, which offers repeatable and statistically convincing measures for evaluating quality metrics. Experimental results demonstrate the usability of the proposed evaluation framework via offline trials.
Starting Page	243
Ending Page	251
Page Count	9
ISSN	20474938
Volume Number	5
e-ISSN	20474946
Issue Number	Issue 3, Sep (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-bmt/5/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2015.0027
Journal	IET Biometrics
Publisher Date	2016-09-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Computer Vision And Image Processing Technique Enrolment Selection Fingerprint Identification Fingerprint Image Quality Assessment Fingerprint Image Quality Evaluation Approach FQA Image Recognition Noisy Information Quality Metric Evaluation Statistical Analysis Statistics
Content Type	Text
Resource Type	Article
Subject	Signal Processing Computer Vision and Pattern Recognition Software

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