A Comparative Study of Fingerprint Image-Quality Estimation Methods

Alonso‐Fernandez, Fernando; Fiérrez, Julián; Ortega‐Garcia, Javier; González-Rodríguez, Joaquín; Fronthaler, H.; Kollreider, K.; Bigün, Josef

doi:10.1109/tifs.2007.908228

Cited by 187 publications

(117 citation statements)

References 32 publications

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“…If quality of image captured is poor that creates problem of unauthentic and lost features, which degrades the performance of the overall system. Therefore, in fingerprint recognition system it is important to estimate the quality and validity of the captured fingerprint images from sensors [25].…”

Section: Finger Print Authenticationmentioning

confidence: 99%

Review of Hand Feature of Unimodal and Multimodal Biometric System

Shaikh¹,

Kolekar²

2016

IJCA

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In this age of digital impersonation, biometric techniques are being used increasingly for authentication technique to prevent unauthorized access. As only biometrics, the authentication of individuals using biological identities, can offer true proof of identity. The increasing interest of biometrics is related to security, forensics and remote managing. Extensive research has been conducted in this area with different techniques. In this paper, unimodal, multimodal and fusion techniques are reviewed for authentication. General TermsSecurity

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Section: Finger Print Authenticationmentioning

confidence: 99%

Review of Hand Feature of Unimodal and Multimodal Biometric System

Shaikh¹,

Kolekar²

2016

IJCA

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“…A number of approaches for fingerprint image quality computation have been described in the literature. A taxonomy is given in [18] (see Fig. 2).…”

Section: Feature Extractionmentioning

confidence: 99%

“…In the present work, we explore the potential of quality assessment (already considered in the literature for multimodal fusion [17], or score rejection [18]), for liveness detection. Thus, a new parameterization based on quality related measures for a software-based solution in fingerprint vitality detection is proposed, and its efficiency to countermeasure direct attacks is evaluated.…”

Section: Introductionmentioning

confidence: 99%

A high performance fingerprint liveness detection method based on quality related features

Galbally

Alonso‐Fernandez

Fiérrez-Aguilar

et al. 2012

Future Generation Computer Systems

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171

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. a b s t r a c tA new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.

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“…Therefore, a preprocessing step is needed to select fingerprint images that will be used for verification. There are many rules to select effective fingerprint images, i.e., foreground size and gray-value contrast [35]. For different purposes, researchers choose different selection criteria.…”

Section: Preprocessing Of Fingerprint Videomentioning

confidence: 99%

Video-based fingerprint verification

Wei

Yin

Ren

et al. 2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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Conventional fingerprint verification systems use only static information. In this paper, fingerprint videos, which contain dynamic information, are utilized for verification. Fingerprint videos are acquired by the same capture device that acquires conventional fingerprint images, and the user experience of providing a fingerprint video is the same as that of providing a single impression. After preprocessing and aligning processes, "inside similarity" and "outside similarity" are defined and calculated to take advantage of both dynamic and static information contained in fingerprint videos. Match scores between two matching fingerprint videos are then calculated by combining the two kinds of similarity. Experimental results show that the proposed video-based method leads to a relative reduction of 60 percent in the equal error rate (EER) in comparison to the conventional single impression-based method. We also analyze the time complexity of our method when different combinations of strategies are used. Our method still outperforms the conventional method, even if both methods have the same time complexity. Finally, experimental results demonstrate that the proposed video-based method can lead to better accuracy than the multiple impressions fusion method, and the proposed method has a much lower false acceptance rate (FAR) when the false rejection rate (FRR) is quite low.

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A Comparative Study of Fingerprint Image-Quality Estimation Methods

Cited by 187 publications

References 32 publications

Review of Hand Feature of Unimodal and Multimodal Biometric System

Review of Hand Feature of Unimodal and Multimodal Biometric System

A high performance fingerprint liveness detection method based on quality related features

Video-based fingerprint verification

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