Fingerprint image analysis: role of orientation patch and ridge structure dictionaries

Jain, Anil K.; Cao, Kai

doi:10.1002/9781118866641.ch15

Cited by 10 publications

(4 citation statements)

References 39 publications

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“…Fingerprint Verification tests were carried out using Neurotechnology SDK on fingerprints of the standard databases mentioned, in their original form (not binarised), and on similar fingerprints binarised with the proposed preprocessing algorithm. Neurotechnology Verifinger SDK was used because the SDK performs OFE [40], enhancement, minutiae extraction and subsequently, verification or identification. If fingerprints are preprocessed before OFE, there will be a progressive improvement in the OFE and later stages, especially in the match scores obtained after verification or identification.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Effective statistical‐based and dynamic fingerprint preprocessing technique

Iloanusi

2016

IET biom.

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Challenges to contextual filtering techniques include difficulties in estimating orientation field in poor quality images and subsequently failure to extract ridges reliably in large regions of low quality. This study proposes a statistical‐based and dynamic fingerprint preprocessing technique for adaptive contrast enhancement and binarisation of fair and poor qualities plain and rolled fingerprints with large regions of low quality, prior to orientation field estimation. The algorithm effectively enhances smudged and faded ridges uniformly in recoverable regions, based on values of statistical variables computed locally in each region. The preprocessing algorithm employs a locally adaptive thresholding approach resulting in enhanced binarised images. The performance of the proposed algorithm was determined by carrying out biometric verification evaluation using a popular commercial biometric matching software, on databases of fingerprints in their original forms, as well as same fingerprints enhanced with the proposed algorithm. Experiments show that fingerprints are uniformly enhanced and binarised; and smudged or faded ridges in recoverable regions made visible. Fingerprint verification evaluation on preprocessed fingerprints resulted in lower error rates in 12 databases. These results show that the proposed algorithm significantly improves recognition.

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Section: Experiments and Resultsmentioning

confidence: 99%

Effective statistical‐based and dynamic fingerprint preprocessing technique

Iloanusi

2016

IET biom.

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“…Jain and Cao [100] summarized the role of the two types of dictionaries, i.e. GlobalDic and LocalizedDic, as representations of prior knowledge about fingerprint patterns, and showed how these dictionaries can be used in latent segmentation and enhancement.…”

Section: Learning-based Methodsmentioning

confidence: 99%

A Survey of the Methods on Fingerprint Orientation Field Estimation

Bian

et al. 2019

IEEE Access

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Fingerprint orientation field (FOF) estimation plays a key role in enhancing the performance of the automated fingerprint identification system (AFIS): accurate estimation of FOF can evidently improve the performance of AFIS. However, despite the enormous attention on the FOF estimation research in the past decades, the accurate estimation of FOFs, especially for poor-quality fingerprints, still remains a challenging task. In this paper, we devote to review and categorization of the large number of FOF estimation methods proposed in the specialized literature, with particular attention to the most recent work in this area. Broadly speaking, the existing FOF estimation methods can be grouped into three categories: gradient-based methods, mathematical models-based methods, and learning-based methods. Identifying and explaining the advantages and limitations of these FOF estimation methods is of fundamental importance for fingerprint identification, because only a full understanding of the nature of these methods can shed light on the most essential issues for FOF estimation. In this paper, we make a comprehensive discussion and analysis of these methods concerning their advantages and limitations. We have also conducted experiments using publically available competition dataset to effectively compare the performance of the most relevant algorithms and methods.

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“…Feng et al [60] proposed to use local dictionaries of orientation patches for later filtering. Cao et al [61] and Jain and Cao [62] used dictionaries of local ridge patches. Jain et al [63] used dedicated dictionaries for application to children.…”

Section: Methods Of Image Enhancementsmentioning

confidence: 99%

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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Fingerprint image analysis: role of orientation patch and ridge structure dictionaries

Cited by 10 publications

References 39 publications

Effective statistical‐based and dynamic fingerprint preprocessing technique

Effective statistical‐based and dynamic fingerprint preprocessing technique

A Survey of the Methods on Fingerprint Orientation Field Estimation

Survey on the impact of fingerprint image enhancement

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