Theoretical Approach to Developing Efficient Algorithms of Fingerprint Enhancement

Khachay, Michael; Pasynkov, Maxim

doi:10.1007/978-3-319-26123-2_8

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…Gottschlich [55] proposed the usage of curved filters and to make use of knowledge about fingerprints. This idea is picked up again by Khachay and Pasynkov [56] and Mei et al . [57].…”

Section: Methods Of Image Enhancementsmentioning

confidence: 97%

Survey on the impact of fingerprint image enhancement

Schuch¹,

Schulz²,

Busch³

2017

IET biom.

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The performance of fingerprint comparison algorithms depends on the reliability and accuracy of the features extracted from the fingerprints. The accuracy of the feature extraction algorithms is assumed to depend on the quality of the fingerprint images. Especially, low-quality images can be challenging for feature extraction algorithms. Image enhancement may allow to extract features more accurately. There is a lack of extensive and quantitative evaluation of image enhancement methods. This study investigates the impact of seven typical image enhancement methods on biometric sample quality and on biometric performance. The interrelation of image quality and biometric performance is investigated on 14 datasets. Biometric quality measures are estimated based on image quality metrics NFIQ1 and NFIQ2.0. Biometric performance is tested using MINDTCT and FingerJetFX for feature extraction and BOZORTH3 for biometric comparison. This work shows that the biometric performance can be improved by image enhancement. The significance of improvements depends on both the quality of the datasets and the feature extraction. Thus, there is no single best improvement algorithm. A correlation of changes in scores and image qualities can only be found on the level of entire datasets. No significant correlation can be found for single biometric comparisons.

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“…Gottschlich [55] proposed the usage of curved filters and to make use of knowledge about fingerprints. This idea is picked up again by Khachay and Pasynkov [56] and Mei et al . [57].…”

Section: Methods Of Image Enhancementsmentioning

confidence: 97%

Survey on the impact of fingerprint image enhancement

Schuch¹,

Schulz²,

Busch³

2017

IET biom.

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show abstract

“…Кроме того, удалось преодолеть недостатки некоторых из ранее опубликованных работ [2,4,7], обзор которых представлен в разделе 1.…”

Section: примеры изображений для демонстрации работы алгоритмаunclassified

Robust Fingerprint Flow Chart Algorithm

2019

CMSE

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“…For example, the use of segmentation allows more accurately determine locations and types of singular points of the analyzed images (Figure 2). Also, segmentation is necessary at the stage of fingerprint image enhancement [4], and at the stage of final identification [5].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks in Application to Segmentation of Fingerprint Images

Pasynkov¹,

Khachay²

2017

dtcse

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Abstract. Segmentation of fingerprint images is one of the most important problems concerned with automatic biometric identification system. Segmentation is used to separate the area of the fingerprint (foreground) from the background and areas that cannot be recovered. We propose a new algorithm for fingerprint segmentation based on convolutional neural networks, binary region labeling technique, and morphological image processing. This approach was tested on public fingerprint dataset provided by Fingerprint Verification Competition (FVC: 2002(FVC: , 2004). The experimental results showed a high quality of segmentation.

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Theoretical Approach to Developing Efficient Algorithms of Fingerprint Enhancement

Cited by 4 publications

References 22 publications

Survey on the impact of fingerprint image enhancement

Survey on the impact of fingerprint image enhancement

Robust Fingerprint Flow Chart Algorithm

Convolutional Neural Networks in Application to Segmentation of Fingerprint Images

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