Fingerprint pore extraction based on Marker controlled Watershed Segmentation

Malathi, S.; Maheswari, S.; Meena, C.

doi:10.1109/iccae.2010.5451426

Cited by 13 publications

(4 citation statements)

References 10 publications

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“…Their algorithm is also computational expensive because it requires a lot of post-processing steps to removing false pores which are not useful for matching. Malathi et al [12] used marker controlled watershed segmentation to effectively identify fingerprint pores. Their algorithm was tested on a database consisting of only 500dpi fingerprint images.…”

Section: Previous Workmentioning

confidence: 99%

Fingerprint pores extractor

Mngenge

Nelufule

Nelwamondo

et al. 2012

2012 National Conference on Computing and Communication Systems

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Automatic Fingerprint Recognition Systems (AFRSs) rely on minutiae position and orientation within the fingerprint image for matching. Minutiae information is highly accurate provided that the fingerprint image matched is of high quality. However, this is not always the case because of diseases and hash working conditions that affect fingerprints. In order to maintain high level of security independent of varying fingerprint image quality research suggests the use of other fingerprint features to compliment minutiae. These are things like ridge contours, sweat pores, dots, and incipient ridges. Sweat pores have been proven as one of the most distinctive among these feature. Thus in order to improve accuracy of AFRSs pores can be fused with minutiae or used alone. Sweat pores have been less utilized in the past due to constraints imposed by fingerprint scanning devices and resolution standards. Recently, progress has been made on both scanning devices and resolution standards to support the use of pores in AFRSs. However, very few techniques exist for extracting, matching and fusing them with minutiae. Matching and fusion can only be possible if pores are available. Some techniques have been proposed to reliable extract pores. However, existing techniques can only work on one resolution i.e. an algorithm proposed and tested on 500dpi cannot work on 1000dpi without minor modifications because pores size change if resolution changes. In addition, existing pore extraction techniques are computationally expensive. In this paper an algorithm to extract feature level 3 (pores) is proposed. The algorithm uses Laplacian of Gaussian (LoG) in Fourier domain in order to reduce computation. The performance of the proposed algorithm is tested on two distinct databases with different resolutions in order to validate its accuracy. The accuracy of the proposed algorithm is further measured using false detection rate (FDR) and true detection rate (TDR). Results show that FDR ranges from 10-35% while TDR ranges from 65-90%.

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Section: Previous Workmentioning

confidence: 99%

Fingerprint pores extractor

Mngenge

Nelufule

Nelwamondo

et al. 2012

2012 National Conference on Computing and Communication Systems

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“…The majority of methods for extracting the coordinates of the pores deal with touch-based fingerprint images. These methods use different techniques to estimate the shape of the pores from the samples, such as: Gabor filters [19], watershed segmentation [32], wavelet transforms [1,19], or morphological operators [5]. To the best of our knowledge, the study presented in [49] is the only attempt of automatic estimation of the pores from latent images.…”

Section: Extraction Of Pores From Fingerprint Imagesmentioning

confidence: 99%

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Labati

Genovese

Muñoz

et al. 2018

Pattern Recognition Letters

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Most fingerprint recognition systems use Level 1 characteristics (ridge flow, orientation, and frequency) and Level 2 features (minutiae points) to recognize individuals. Level 3 features (sweat pores, incipient ridges and ultra-thin characteristics of the ridges) are less frequently adopted because they can be extracted only from high resolution images, but they have the potential of improving all the steps of the biometric recognition process. In particular, sweat pores can be used for quality assessment, liveness detection, biometric matching in live applications, and matching of partial latent fingerprints in forensic applications. Currently, each type of fingerprint acquisition technique (touch-based, touchless, or latent) requires a different algorithm for pore extraction. In this paper, we propose the first method in the literature able to extract the coordinates of the pores from touch-based, touchless, and latent fingerprint images. Our method uses specifically designed and trained Convolutional Neural Networks (CNN) to estimate and refine the centroid of each pore. Results show that our method is feasible and achieved satisfactory accuracy for all the types of evaluated images, with a better performance with respect to the compared state-of-the-art methods.

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“…For the purpose of recognition, methods for extracting the positions of the pores use Gabor Filters [25], wavelet transforms [25], [26], watershed segmentation [27], or morphological operators [28]. Matching methods specifically designed for pores use ICP [25], RANSAC [29], local binary patterns [30], Delaunay triangulation [26], or local features [29], [31].…”

Section: Related Workmentioning

confidence: 99%

Towards touchless pore fingerprint biometrics: A neural approach

Genovese

Muñoz

Piuri

et al. 2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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Touchless fingerprint recognition systems are being increasingly used for a fast, hygienic, and distortion-free recognition. However, due to the greater complexity of the algorithms required for processing touchless fingerprint samples, currently only Level 1 and Level 2 features are being used for recognition, and Level 3 features are used only in touch-based optical devices with about 1000 ppi resolution. In this paper, we propose the first innovative method in the literature able to extract Level 3 features, in particular sweat pores, from fingerprint images captured with a touchless acquisition using a commercial offthe-shelf camera. The method uses image processing algorithms to extract a set of candidate sweat pores. Then, computational intelligence techniques based on neural networks are used to learn the local features of the real pores, and select only the actual sweat pores from the set of candidate points. The results show the validity of the proposed methodology, with the majority of the pores correctly extracted, indicating that a touchless fingerprint recognition using Level 3 features is feasible.

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Fingerprint pore extraction based on Marker controlled Watershed Segmentation

Cited by 13 publications

References 10 publications

Fingerprint pores extractor

Fingerprint pores extractor

A novel pore extraction method for heterogeneous fingerprint images using Convolutional Neural Networks

Towards touchless pore fingerprint biometrics: A neural approach

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