Objective Fingerprint Image Quality Assessment using Gabor Spectrum Approach

Al-Tarawneh, Mokhled S.; Woo, Wai Lok; Dlay, S.S.

doi:10.1109/icdsp.2007.4288565

Cited by 6 publications

(6 citation statements)

References 5 publications

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“…Most of the work has involved various wavelet representations of the fingerprint image directed toward evaluation of the image quality, e.g. [3][4][5][6]. Other work such as that of Maio [7] and Yin [8] looks specifically at ridge spacing and distinctness of the pattern, mainly with respect to characterization of the fingerprint ridge structure as a distinct texture to be used in a recognition scheme as well as for evaluation of image quality.…”

Section: Introductionmentioning

confidence: 99%

A 1D spectral image validationverification metric for fingerprints

Libert¹,

Grantham²,

Orandi³

2009

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Section: Introductionmentioning

confidence: 99%

A 1D spectral image validationverification metric for fingerprints

Libert¹,

Grantham²,

Orandi³

2009

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“…O desempenho e interoperabilidade dos sistemas biométricos dependem da qualidade do sinal adquirido [2]. Entretanto, uma metodologia para a estimação da qualidade 1 http://www.tbsinc.com/ das amostras biométricas coletadas são de fundamental importância, principalmente para se obter elevadosíndices de acerto.…”

Section: Avaliação Da Qualidade De Impressões Digitaisunclassified

Projeto e implementação de uma rede neural artificial para detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque

Zaghetto

Aguiar

Zaghetto

et al. 2015

Anais Do Simpósio Brasileiro De Sistemas De Informação (SBSI)

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This paper presents a method based on Artificial Neural Network that evaluates the rotational bad-positioning of fingers on touchless multiview fingerprinting devices. The objective is to determine whether the finger is rotated or not, since a proper positioning of the finger is mandatory for high fingerprint matching rates. A test set of 9000 acquired images has being used to train, validate and test the proposed multilayer Artificial Neural Network classifier. To our knowledge, there is no definitive method that addressed the problem of fingerprint quality on touchless multiview scanners. The proposed finger rotation detection here presented is one of the steps that must be taken into account if a future automatic image quality assessment method is to be considered. Average results show that: (a) our classifier correctly identifies bad-positioning in approximately 94% of cases; and (b) if bad-positioning is detected, the rotation angle is correctly estimated in 90% evaluations.

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“…Our proposed method and methodology differs from that in [2] as we do not subdivide the input into blocks and we do not use a subjective measure to compare the performance of the algorithm. Instead we work in a pointwise manner and use an objective performance assessment, thus ensuring reproducability of results.…”

Section: Introductionmentioning

confidence: 97%

“…Locally, the ridge orientation certainty level [12], local orientation [4], or the blockwise similarity to Gabor filter responses [2] can be applied as indicators of quality. In a similar manner, the discrete Fourier transform can be analyzed [13] to give an impression of the global quality.…”

Section: Introductionmentioning

confidence: 99%

Gabor filters as candidate quality measure for NFIQ 2.0

Olsen

Busch³

2012

2012 5th IAPR International Conference on Biometrics (ICB)

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Quality assessment of biometric fingerprint images is necessary to ensure high biometric performance in biometric recognition systems. We relate the quality of a fingerprint sample to the biometric performance to ensure an objective and performance oriented benchmark. The proposed quality metric is based on Gabor filter responses and is evaluated against eight contemporary quality estimation methods on four datasets using sample utility derived from the separation of genuine and imposter distributions as benchmark. The proposed metric shows performance and consistency approaching that of the composite NFIQ quality assessment algorithm and is thus a candidate for inclusion in a feature vector introducing the NFIQ 2.0 metric.

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Objective Fingerprint Image Quality Assessment using Gabor Spectrum Approach

Cited by 6 publications

References 5 publications

A 1D spectral image validationverification metric for fingerprints

A 1D spectral image validationverification metric for fingerprints

Projeto e implementação de uma rede neural artificial para detecção do mal-posicionamento rotacional de dedos em dispositivos de captura de impressões digitais multivista sem toque

Gabor filters as candidate quality measure for NFIQ 2.0

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