Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms

Peralta, Daniel; Galar, Mikel; Triguero, Isaac; Miguel-Hurtado, Oscar; Benítez, José Manuel; Herrera, Francisco

doi:10.1016/j.engappai.2014.02.016

Cited by 30 publications

(13 citation statements)

References 24 publications

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“…The suggested algorithm depends on the pattern approach that comprises the whole formation of the fingerprint image. This algorithm makes use of both local and globally conventional fingerprint image [17]. Application of dust and oil contributes to noise on the image of the fingerprints when detecting the fingerprints using the sensors.…”

Section: A) B) Fingerprint Segmentation: a -Input Image; B -Output Imagementioning

confidence: 99%

A Fingerprint Matching Algorithm

Wahhab¹,

Alanssari²,

Rozhina³

et al. 2019

CTAC&R

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Fingerprint pattern knowledge is largely applied in many fields such as access control and identity administration. This is however associated with some problem of automatic fingerprint recognition and therefore this has rendered to the use of the most known method that is biometric identification. Every finger of the hand shows a different pattern of ridges and depression different from the other finger and this pattern remains sole and constant thus helping in identity since fingerprint pattern from one person is different to that of another person. This pattern may alter whenever there are cuts and bruises in the outer part of the finger. Fingerprint pattern recognition method includes the following steps: firstly, matching of the fingerprint which includes the pattern based method and the minutiae method. Secondly, the used algorithm in the recognition and comparing of the fingerprint images. Thirdly, the image enhancement process that helps to improve the quality of the fingerprint pattern and forth the reduction of the size of the image which includes identification of the region of small minutiae and actual minutiae. The objective of this research is recognition of the fingerprint pattern.

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Section: A) B) Fingerprint Segmentation: a -Input Image; B -Output Imagementioning

confidence: 99%

A Fingerprint Matching Algorithm

Wahhab¹,

Alanssari²,

Rozhina³

et al. 2019

CTAC&R

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“…Biometrics may use physical or behavioral characteristics for identification purposes, and different alternatives have been explored over the years: fingerprint [3,4,5], hand geometry [6,7], palmprint [8], voice [9,10], face [11,12,13], and handwritten signature [14]. Among those, face stands out for its acceptability and recognition cost, turning out to be one of the best option for a wide range of applications, from low-security uses (e.g., social media and smartphone access control) to high-security applications (e.g., border control and video surveillance in critical places).…”

Section: Introductionmentioning

confidence: 99%

How far did we get in face spoofing detection?

Souza

Oliveira

Pamplona

et al. 2018

Engineering Applications of Artificial Intelligence

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The growing use of control access systems based on face recognition shed light over the need for even more accurate systems to detect face spoofing attacks. In this paper, an extensive analysis on face spoofing detection works published in the last decade is presented. The analyzed works are categorized by their fundamental parts, i.e., descriptors and classifiers. This structured survey also brings a comparative performance analysis of the works considering the most important public data sets in the field. The methodology followed in this work is particularly relevant to observe temporal evolution of the field, trends in the existing approaches, * Corresponding author: Luciano Oliveira, tel. +55 71 3283-9472Email addresses: luiz.otavio@ufba.br (Luiz Souza), lrebouca@ufba.br (Luciano Oliveira), mauricio@dcc.ufba.br (Mauricio Pamplona), papa@fc.unesp.br (Joao Papa) to discuss still opened issues, and to propose new perspectives for the future of face spoofing detection.

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“…Biometric verification/identification methods are based on the analysis of popular physical features (e.g., iris, retina, friction ridges, fingerprints, blood vessel pattern, ear shape, etc.) [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

The Method of Automatic Knuckle Image Acquisition for Continuous Verification Systems

Doroz

2018

Symmetry

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The paper proposes a method of automatic knuckle image acquisition for continuous verification systems. The developed acquisition method is dedicated for verification systems in which the person being verified uses a computer keyboard. This manner of acquisition enables registration of the knuckle image without interrupting the user's work for the time of acquisition. This is an important advantage, unprecedented in the currently known methods. The process of the automatic location of the finger knuckle can be considered as a pattern recognition approach and is based on the analysis of symmetry and similarity between the reference knuckle patterns and live camera image. The effectiveness of the aforesaid approach has been tested experimentally. The test results confirmed its high effectiveness. The effectiveness of the proposed method was also determined in a case where it is a part of a multi-biometric method.

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Minutiae filtering to improve both efficacy and efficiency of fingerprint matching algorithms

Cited by 30 publications

References 24 publications

A Fingerprint Matching Algorithm

A Fingerprint Matching Algorithm

How far did we get in face spoofing detection?

The Method of Automatic Knuckle Image Acquisition for Continuous Verification Systems

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