Fingerprint-Quality Index Using Gradient Components

Lee, Sang Hoon; Choi, Heeseung; Choi, Kyoungtaek; Kim, Jaihie

doi:10.1109/tifs.2008.2007245

Cited by 32 publications

(15 citation statements)

References 17 publications

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“…Therefore, they are suited to operational law enforcement agency setups and only optimised and tested for contact scanners [10][11][12][13] but not fingermarks. This has resulted in various algorithms tuned to a capture resolution of 500 ppi.…”

Section: Introductionmentioning

confidence: 99%

Discriminating fingermarks with evidential value for forensic comparison

Kotzerke

Davis

Hayes

et al. 2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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Law enforcement agencies all around the world are using biometrics and especially fingerprints to solve and fight crime. Often forensic experts are needed to record fingermarks at crime scenes and to ensure that those captured are of forensic value. In times of increased demand for forensic services, this process needs to be automated and streamlined as much as possible to improve efficiency and reduce workload.Hence, we investigate if the forensic evidential value (suitability for forensic analysis and/or examination) of fingermark images can be determined at an early stage automatically without any expert involvement, especially when using a mobile phone camera. We explore different factors such as the capture device and the constraints inferred, image feature sets and classifiers used, and their interplay.A database of 1,428 pseudo fingermarks has been collected and its ground truth, whether a mark is of forensic value or not, has been determined by 3 experts. The lowest equal error rate achieved, when using a mobile phone to capture the marks, is 13.62%.These promising results suggest that it might be possible to streamline forensic procedures by the application of an independent automated tool to assist with certain tasks.

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Section: Introductionmentioning

confidence: 99%

Discriminating fingermarks with evidential value for forensic comparison

Kotzerke

Davis

Hayes

et al. 2015

3rd International Workshop on Biometrics and Forensics (IWBF 2015)

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“…In [22] the authors propose a novel quality-checking algorithm which considers the condition of the input fingerprints and the orientation estimation errors. First, the 2D gradients of the fingerprint image is separated into two sets of 1D gradients, and then the shape of the probability density functions of these gradients is measured in order to determine the fingerprint quality.…”

Section: Remarks On Fingerprint Image Quality Evaluation Methodsmentioning

confidence: 99%

Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users

Vitello

Conti

Vitabile

et al. 2015

Mobile Information Systems

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The way people access resources, data and services, is radically changing using modern mobile technologies. In this scenario, biometry is a good solution for security issues even if its performance is influenced by the acquired data quality. In this paper, a novel embedded automatic fingerprint authentication system (AFAS) for mobile users is described. The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures. The system is focused on the quality evaluation of the raw acquired fingerprint, identifying areas of poor quality. Using this approach, no image enhancement process is needed after the fingerprint acquisition phase. The Agility RC2000 board has been used to prototype the embedded device. Due its different image resolution and quality, the experimental tests have been conducted on both PolyU and FVC2002 DB2-B free databases. Experimental results show an interesting trade-off between used resources, authentication time, and accuracy rate. The best achieved false acceptance rate (FAR) and false rejection rate (FRR) indexes are 0% and 6.25%, respectively. The elaboration time is 62.6 ms with a working frequency of 50 MHz.

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“…Many quality evaluation techniques designed for touchbased fingerprint images are based on the evaluation of local features. The method described in [12] evaluates the probability density function (PDF) of local regions. The technique presented in in [13] computes the used features by applying a set of Gabor filters with different orientations to the local areas of the image.…”

Section: Related Workmentioning

confidence: 99%

Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach

Labati

Genovese

Piuri

et al. 2012

The 2012 International Joint Conference on Neural Networks (IJCNN)

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Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.

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Fingerprint-Quality Index Using Gradient Components

Cited by 32 publications

References 17 publications

Discriminating fingermarks with evidential value for forensic comparison

Discriminating fingermarks with evidential value for forensic comparison

Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users

Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach

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