Gabor filters as candidate quality measure for NFIQ 2.0

Olsen, Martin Aastrup; Xu, Haiyun; Busch, Christoph

doi:10.1109/icb.2012.6199802

Cited by 24 publications

(21 citation statements)

References 12 publications

(11 reference statements)

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“…3.4.3 Gabor (GAB) quality: The GAB quality feature [34] operates on a per-pixel basis by calculating the standard deviation of the Gabor filter bank responses. The size of the filter bank is used to determine a number of filters oriented evenly across the half circle.…”

Section: Local Finger Image Qualitymentioning

confidence: 99%

Finger image quality assessment features – definitions and evaluation

Olsen¹,

Šmida

Busch

2016

IET Biometrics

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Finger image quality assessment is a crucial part of any system where a high biometric performance and user satisfaction is desired. Several algorithms measuring selected aspects of finger image quality have been proposed in the literature, yet only few of them have found their way into quality assessment algorithms used in practice. The authors provide comprehensive algorithm descriptions and make available implementations of adaptations of ten quality assessment algorithms from the literature which operates at the local or the global image level. They evaluate the performance on four datasets in terms of the capability in determining samples causing false non-matches and by their Spearman correlation with sample utility. The authors' evaluation shows that both the capability in rejecting samples causing false non-matches and the correlation between features varies depending on the dataset.

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Section: Local Finger Image Qualitymentioning

confidence: 99%

Finger image quality assessment features – definitions and evaluation

Olsen¹,

Šmida

Busch

2016

IET Biometrics

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“…Fernandez et al (Alonso-Fernandez et al, 2007) and Olsen (Olsen et al, 2012) respectively calculated Pearson and Spearman correlation coefficients between different quality metrics to observe their behavior. Similarly, we investigate the behavior of the proposed quality metric through the Pearson correlation coefficients, by which the parameters are appropriately selected as well.…”

Section: Parameter Settingsmentioning

confidence: 99%

Quality Assessment of Fingerprints with Minutiae Delaunay Triangulation

Yao

Bars

Charrier

et al. 2015

Proceedings of the 1st International Conference on Information Systems Security and Privacy

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This article proposes a new quality assessment method of fingerprint, represented by only a set of minutiae points. The proposed quality metric is modeled with the convex-hull and Delaunay triangulation of the minutiae points. The validity of this quality metric is verified on several Fingerprint Verification Competition (FVC) databases by referring to an image-based metric from the state of the art (considered as the reference). The experiments of the utility-based evaluation approach demonstrate that the proposed quality metric is able to generate a desired result. We reveal the possibility of assessing fingerprint quality when only the minutiae template is available.

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“…The quality score decreases as the difference between the dominant ridge orientation of the block and its 8 neighboring blocks increases. A block wise Gabor-based quality feature was proposed in [16] and a point wise Gabor based quality feature was proposed in [15].…”

Section: Background and Related Workmentioning

confidence: 99%

Towards NFIQ II Lite :

Tabassi¹,

Olsen²,

Макаров³

et al. 2013

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Fingerprint quality assessment is a crucial task which needs to be conducted accurately in various phases in the biometric enrolment and recognition processes. Neglecting quality measurement will adversely impact the accuracy and efficiency of biometric recognition systems (e.g., verification and identification of individuals). Measuring and reporting quality allows processing enhancements to increase probability of detection and track accuracy while decreasing probability of false alarms. Aside from predictive capabilities with respect to the recognition performance, another important design criteria for a quality assessment algorithm is to meet the low computational complexity requirement of mobile platforms used by military and police forces in national biometric systems. We propose a computationally efficient means of predicting biometric performance based on a combination of unsupervised and supervised machine learning techniques. We train a selforganizing map (SOM) to cluster blocks of fingerprint images based on their spatial information content. The output of the SOM is a high-level representation of the finger image, which forms the input to a Random Forest trained to learn the relationship between the SOM output and biometric performance. The quantitative evaluation performed demonstrates that our proposed quality assessment algorithm is a reasonable predictor of performance. The open source code of our algorithm will be posted at http://www.nist.gov/itl/iad/ig/development_nfiq_ 2.cfm.

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Gabor filters as candidate quality measure for NFIQ 2.0

Cited by 24 publications

References 12 publications

Finger image quality assessment features – definitions and evaluation

Finger image quality assessment features – definitions and evaluation

Quality Assessment of Fingerprints with Minutiae Delaunay Triangulation

Towards NFIQ II Lite :

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