Adaptive fingerprint pore modeling and extraction

Zhao, Qijun; Zhang, David; Luo, Nan

doi:10.1016/j.patcog.2010.02.016

Cited by 101 publications

(71 citation statements)

References 17 publications

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“…Let θ be the orientation parameter that can be computed by the local ridge orientation [6]. Here θ(x, y) represents the local ridge at pixel (x, y) and is calculated in a non-overlapping block-wise manner.…”

Section: Virtual Core Point Detectionmentioning

confidence: 99%

“…The overall performance of AFIS mostly relies on extracting features and its efficient matching mechanisms. It still deserves a much more advanced and accessible high volume fingerprint database that takes into account the computational speed and accuracy of matching [5,6]. To extract and compare of an image for matching, roughly three techniques can be categorized: minutiae-based, correlation-based, and hybrid explained in [7].…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

Basak¹,

Islam²,

Amin³

2012

Journal of Information Processing Systems

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Abstract-Different types of fingerprint detection algorithms that are based on extraction of minutiae points are prevalent in recent literature. In this paper, we propose a new algorithm to locate the virtual core point/centroid of an image. The Euclidean distance between the virtual core point and the minutiae points is taken as a random variable. The mean, variance, skewness, and kurtosis of the random variable are taken as the statistical parameters of the image to observe the similarities or dissimilarities among fingerprints from the same or different persons. Finally, we verified our observations with a moment parameter-based analysis of some previous works.

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Section: Virtual Core Point Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

Basak¹,

Islam²,

Amin³

2012

Journal of Information Processing Systems

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show abstract

“…The fingerprint image is partitioned into blocks and a local pore model is determined for each block [1].…”

Section: Introductionmentioning

confidence: 99%

Scanner based Fingerprint Matching system for Security Applications

2016

IJSR

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Abstract:In the presented work, it is proposed to enhance the finger print image quality based on an adaptive fingerprint enhancement method that is based on contextual filtering. The term adaptive implies that parameters of the method are automatically adjusted based on the input fingerprint image. Once the finger print image is enhanced at the required level, the pores are extracted based on segmentation of the finger print image by eliminating the finger print area above a threshold limit. The pores location and then interdistances are computed and stored in a data base. Further, the minutiae are extracted and again their location and inter-distances are computed and stored in a data base. Same procedure is repeated for the query image and standard deviation is computed between the inter-distance of the query image and data base images. The finger print information based on pores and minutiae are fused together in order to get the matching score.

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“…Compared with the passwords or ID cards based solutions, biometrics authentication is much more preferable and reliable to those applications requiring high security. In the past decades, various biometrics traits, such as face [1], [2], fingerprint [3], [4], iris [5], [6], etc., have been widely studied. Meanwhile, hand-based biometrics methods are popular in the biometrics community, and techniques such as palmprint [7]- [9], hand geometry [10], hand vein [11], etc., have been developed.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction Based Finger-Knuckle-Print Verification With Score Level Adaptive Binary Fusion

Gao

Zhang

Yang

et al. 2013

IEEE Trans. on Image Process.

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Abstract-Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentication with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.

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Adaptive fingerprint pore modeling and extraction

Cited by 101 publications

References 17 publications

A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

A New Approach to Fingerprint Detection Using a Combination of Minutiae Points and Invariant Moments Parameters

Scanner based Fingerprint Matching system for Security Applications

Reconstruction Based Finger-Knuckle-Print Verification With Score Level Adaptive Binary Fusion

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