Fingerprint image quality

Tabassi, Elham; Grother, Patrick J.

doi:10.6028/nist.ir.7151

Cited by 212 publications

(81 citation statements)

References 29 publications

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“…Most of them are based on operational procedures for computing local orientation coherence measures [12]. Some examples include: local Gabor-based filtering [10,13], local and global spatial features [14], directional measures [15], classification-based approaches [16], and local measures based on intensity gradient [17]. In the present work we use the global quality index computed in the spatial frequency domain detailed in [17], which is summarized below.…”

Section: Assessment Of Fingerprint Image Qualitymentioning

confidence: 99%

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Fiérrez-Aguilar

Chen

Ortega‐Garcia

et al. 2005

Advances in Biometrics

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract. The effect of image quality on the performance of fingerprint verification is studied. In particular, we investigate the performance of two fingerprint matchers based on minutiae and ridge information as well as their score-level combination under varying fingerprint image quality. The ridge-based system is found to be more robust to image quality degradation than the minutiae-based system. We exploit this fact by introducing an adaptive score fusion scheme based on automatic quality estimation in the spatial frequency domain. The proposed scheme leads to enhanced performance over a wide range of fingerprint image quality.

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Section: Assessment Of Fingerprint Image Qualitymentioning

confidence: 99%

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Fiérrez-Aguilar

Chen

Ortega‐Garcia

et al. 2005

Advances in Biometrics

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“…We used 1,844 rolled fingerprint pairs in NIST SD4 2 [2] and compared the LFIQ to the AFIS tenprint quality measure and the NFIQ [19]. Again, the proposed LFIQ showed a comparable result to the two tenprint quality measures.…”

Section: Resultsmentioning

confidence: 99%

“…Most algorithms for tenprint quality assessment utilize (i) local properties (e.g., local ridge quality in terms of clarity, orientation, and frequency) and (ii) global properties (e.g., continuity of orientation field or energy concentration in the frequency domain over the entire fingerprint) [5]. As an example, NIST Fingerprint Image Quality (NFIQ) is based on the size of foreground region, the total number of minutiae, minutiae count at different minutiae quality levels, and the size of the fingerprint region at different ridge quality levels [19].…”

Section: Introductionmentioning

confidence: 99%

LFIQ: Latent fingerprint image quality

Yoon

Cao

Liu

et al. 2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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“…Lim et al [6] combined local and global spatial features to detect low quality and invalid fingerprint images. The most recent work by Tabassi et al [1] presented a novel definition of fingerprint quality as a predictor for matching performance. They consider quality assessment as a classification problem and use the quality of extracted features to estimate the quality label of a fingerprint image.…”

Section: Introductionmentioning

confidence: 99%

“…Poor quality fingerprint images often result in spurious and missed features, and thus severely degrade the performance of an authentication system by increasing the false reject and false accept rates. Recently, NIST [1] has shown that the performance of a fingerprint authentication system is mostly affected, among other factors, by fingerprint image quality. Therefore, it is desirable to assess the quality of a fingerprint image to improve the overall performance of a fingerprint authentication system.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Quality Indices for Predicting Authentication Performance

Chen

Dass

Jain

2005

Lecture Notes in Computer Science

202

150

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Abstract. The performance of an automatic fingerprint authentication system relies heavily on the quality of the captured fingerprint images. In this paper, two new quality indices for fingerprint images are developed. The first index measures the energy concentration in the frequency domain as a global feature. The second index measures the spatial coherence in local regions. We present a novel framework for evaluating and comparing quality indices in terms of their capability of predicting the system performance at three different stages, namely, image enhancement, feature extraction and matching. Experimental results on the IBM-HURSLEY and FVC2002 DB3 databases demonstrate that the global index is better than the local index in the enhancement stage (correlation of 0.70 vs. 0.50) and comparative in the feature extraction stage (correlation of 0.70 vs. 0.71). Both quality indices are effective in predicting the matching performance, and by applying a quality-based weighting scheme in the matching algorithm, the overall matching performance can be improved; a decrease of 1.94% in EER is observed on the FVC2002 DB3 database.

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Fingerprint image quality

Cited by 212 publications

References 29 publications

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

Incorporating Image Quality in Multi-algorithm Fingerprint Verification

LFIQ: Latent fingerprint image quality

Fingerprint Quality Indices for Predicting Authentication Performance

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