Fingerprint image processing using neural networks

Leung, Michael; Engeler, W.E.; Frank, Per

doi:10.1109/tencon.1990.152677

Cited by 27 publications

(15 citation statements)

References 4 publications

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“…Han et al proposed minutiae points and similarity between fingerprints based framework for personal identification [9]. Leung et al used Gabor filter and neural network techniques for extracting the features of co mplex fingerprint images [19].…”

Section: Literature Reviewmentioning

confidence: 99%

A Soft Computing Technique for Improving the Fidelity of Thumbprints Based Identification Systems

Mishra¹,

Agrawal²

2016

IJISA

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Abstract-With the advent of new thumbprint identification techniques, accurate personal identificat ion is now easy and cheaper with appro ximately zero false acceptance rates. This paper focuses on developing an advance feature for thumbprint based identificat ion systems with the help of soft computing and 2D transformation which makes the technique more flexib le and Fidel. The thu mbprint images of individuals were scanned with the help of H3 T&A terminal for co llect ing self generated datasets. The thumbprints of self generated and standard datasets were trained to form a refined set which includes linear and angular displacements of thumbprint images. The new obtained features of refined datasets were stored in the database for further identification.In the proposed technique, the minutiae coordinates and orientation angles of the thumbprint of a person to be identified are co mputed and merged together for comparison. The minutia coordinates and orientation angles of a person are compared with the minutiae trained set values stored in the database at different linear and angular rotations for identity verification. The proposed technique was tested on fifty persons self generated and standard datasets of FVC2002, FVC2004 and CASIA databases. In the experimentation and result analysis we observed that the proposed technique accurately identifies a person on the basis of minutiae features of a thumbprint with lo w FNM R (False NonMatch Rate) values.Index Terms-Thumbprint identification, soft computing, 2D t ransformat ion, minutiae coordinates, minutiae direction, trained set and Thumbprint patterns.

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Section: Literature Reviewmentioning

confidence: 99%

A Soft Computing Technique for Improving the Fidelity of Thumbprints Based Identification Systems

Mishra¹,

Agrawal²

2016

IJISA

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“…Jain presented a formula that Gabor function is used in fingerprint enhancement, taking fingerprint ridge orientation and ridge frequency as filtering parameters [3]. In addition, M. T. Leung introduced a neural network based approach to minutiae detection by employing a multilayer perception in analyzing the output of a rank of Gabor's filters applied to the gray scale image [5]. To binarize the fingerprint image, several thresholding methods, local thresholding for instance, have been proposed.…”

Section: Intoductionmentioning

confidence: 99%

Fingerprint Ridge Line Reconstruction

Qi¹

2005

Intelligent Information Processing II

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Abstract:Reconstruction of fingerprint ridge lines is a critical pre-processing step in the identification of poor quality fingerprint images. This paper presents a new fingerprint ridge line reconstruction approach by way of ridge line tracing. In our research, the fingerprint ridge line in a gray scale image is viewed as a track of a ridge segment moving along the ridge. The curve tracing problem is solved by the target tracking technique in computer vision. We first formulate the model of fingerprint ridge line segments and then apply a target tracking method to trace each of the ridge lines. In addition, a feedback technique is adopted to correct the fingerprint directional image in each tracing step in order to improve tracing accuracy. By connecting all the traced ridge line segments, a polyline reconstruction of the ridge line can be obtained. We objectively assess the performance of this approach by using NIST fingerprint images.

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“…In "binarization-based" methods [20], the fingerprint image is binarized and morphologically analyzed for minutiae. In "direct grayscale" methods, mainly the image gradient and local grayscale neighborhood are used to locate minutiae, either by tracking ridges [21], or by classifying directional filter responses [22]. The direction of a minutia is mostly inherited from the associated ridge.…”

Section: Introductionmentioning

confidence: 99%

Local Features for Enhancement and Minutiae Extraction in Fingerprints

Fronthaler

Kollreider

Bigün

2008

IEEE Trans. on Image Process.

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Abstract-Accurate fingerprint recognition presupposes robust feature extraction which is often hampered by noisy input data. We suggest common techniques for both enhancement and minutiae extraction, employing symmetry features. For enhancement, a Laplacian-like image pyramid is used to decompose the original fingerprint into sub-bands corresponding to different spatial scales. In a further step, contextual smoothing is performed on these pyramid levels, where the corresponding filtering directions stem from the frequency-adapted structure tensor (linear symmetry features). For minutiae extraction, parabolic symmetry is added to the local fingerprint model which allows to accurately detect the position and direction of a minutia simultaneously. Our experiments support the view that using the suggested parabolic symmetry features, the extraction of which does not require explicit thinning or other morphological operations, constitute a robust alternative to conventional minutiae extraction. All necessary image processing is done in the spatial domain using 1-D filters only, avoiding block artifacts that reduce the biometric information. We present comparisons to other studies on enhancement in matching tasks employing the open source matcher from NIST, FIS2. Furthermore, we compare the proposed minutiae extraction method with the corresponding method from the NIST package, mindtct. A top five commercial matcher from FVC2006 is used in enhancement quantification as well. The matching error is lowered significantly when plugging in the suggested methods. The FVC2004 fingerprint database, notable for its exceptionally low-quality fingerprints, is used for all experiments.

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Fingerprint image processing using neural networks

Cited by 27 publications

References 4 publications

A Soft Computing Technique for Improving the Fidelity of Thumbprints Based Identification Systems

A Soft Computing Technique for Improving the Fidelity of Thumbprints Based Identification Systems

Fingerprint Ridge Line Reconstruction

Local Features for Enhancement and Minutiae Extraction in Fingerprints

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