Self-Organizing Maps for Fingerprint Image Quality Assessment

Olsen, Martin Aastrup; Tabassi, Elham; Макаров, А. А.; Busch, Christoph

doi:10.1109/cvprw.2013.28

Cited by 25 publications

(15 citation statements)

References 8 publications

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“…Then, a neural network classifier analyzes the features and assigns the final quality value. Currently, a new version of this standard, NFIQ 2.0, is under development [Olsen et al 2013].…”

Section: A:15mentioning

confidence: 99%

Biometric Recognition in Automated Border Control

et al. 2016

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The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on a real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.

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“…Then, a neural network classifier analyzes the features and assigns the final quality value. Currently, a new version of this standard, NFIQ 2.0, is under development [Olsen et al 2013].…”

Section: A:15mentioning

confidence: 99%

Biometric Recognition in Automated Border Control

et al. 2016

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“…In another research, self-organizing maps (SOM) are used to classify local regions of a fingerprint to [3] use the difference of two Butterworth filters to obtain a soft bandpass filter that captures the strength (and thereby quality) of the ridges. http://jivp.eurasipjournals.com/content/2014/1/34 different quality labels [53]. A SOM is trained to cluster blocks of fingerprints based on their spatial information to create a high-level representation of the fingerprint.…”

Section: Fingerprint Quality Assessmentmentioning

confidence: 99%

Biometric quality: a review of fingerprint, iris, and face

2014

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Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

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“…Alguns dispositivos de captura podem ser melhores do que outros, sendo até imunes a fatores que afetam diretamente a qualidade da imagem produzida, mas o problema em se obter imagens com boa qualidade ainda permitem avanços. Em trabalhos recentes, vários problemas que afetam a qualidade da impressão digital (como por exemplo toques com o sensor inconsistentes, não-uniformes e irreprodutíveis [15]) propõem técnicas que resolvem estes problemas ( [6,4,5]). Sendo assim, a análise da avaliação da qualidade são originárias dos mesmos princípios dos sistemas baseados na captura com o toque do dedo do usuário no sensor, estas propostas pelo National Institute of Standards and Technology (NIST) [20], fazendo necessária na utilização de sistemas sem toque se adequem aos mesmos níveis de exigências do sistema com toque.…”

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

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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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Self-Organizing Maps for Fingerprint Image Quality Assessment

Cited by 25 publications

References 8 publications

Biometric Recognition in Automated Border Control

Biometric Recognition in Automated Border Control

Biometric quality: a review of fingerprint, iris, and face

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

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