Singular points detection in fingerprint images using Gabor transform

Lee, Chih-Jen; Jeng, I-Homg; Yang, Tai-Ning; Chen, Chun‐Jung; Lin, Keng-Li

doi:10.1109/icosp.2008.4697554

Cited by 3 publications

(2 citation statements)

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“…The method also provides prediction in interpretations of probability rather than binary decision. Principal Gabor Basis Function (PGBF)-based approach is used in (Chih-Jen et al, 2009) to extract cores, deltas, and minutiae from fingerprint images. Poincare index was also used to classify cores, deltas, or minutiae points.…”

Section: Synopsis Of the Existing Singular Point Detection Workmentioning

confidence: 99%

Review and Evaluation of Fingerprint Singular Point Detection Algorithms

Babatunde

Ojo²

2014

BJAST

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Fingerprint has continued to enjoy dominance over other biometrics like face, iris, nose, signature among others in human verification and authentication. This is promoted by its major characteristics which include availability, uniqueness, consistency and reliability. Fingerprint verification and authentication involves the serial stages of enrolment, processing and matching. Enrolment through a number of fingerprint capturing devices helps to read the fingerprint into a target location from where processing takes place. Processing of a fingerprint image involves enhancement, feature extraction and singular point detection. Matching of fingerprint is performed based on the extraction results to establish its source or similarity level. The extraction of true singular (core and delta) and feature points is paramount for a true fingerprint matching and a number of algorithms had been formulated to accomplish it. This paper presents a review and evaluation of commonly known fingerprint singular point detection algorithms with emphasis on methodologies, strengths and weaknesses.

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Section: Synopsis Of the Existing Singular Point Detection Workmentioning

confidence: 99%

Review and Evaluation of Fingerprint Singular Point Detection Algorithms

Babatunde

Ojo²

2014

BJAST

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“…In [6] a detection method which uses an extension to Gabor filters is applied to detect discontinuities in a fingerprint image. The discontinuities are interpreted as features, and it is not immediately possible to distinguish whether the feature is a bifurcation or an endpoint.…”

Section: B Feature Detectionmentioning

confidence: 99%

Convolution approach for feature detection in topological skeletons obtained from vascular patterns

Olsen

Hartung²,

Busch

et al. 2011

2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

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Abstract-In image processing connected structures can be reduced to an abstract binary skeleton. These skeletons are 1-pixel wide structures which retain the topology of the segmented image. They are used for computer vision, edge detection or high level feature extraction for example in biometric systems. In this paper a fast method on how to extract specific feature points from skeletonized structures is presented. The convolution of the skeleton image with a bi-dimensional mask of size MxN enables us to identify arbitrary structures of the mask size in the skeleton. Of special interest are branch and endpoints of the skeletons to get high level features for biometric comparisons. The problem can here be reduced to the following: in an 8-connected skeleton within a 3x3 mask there are 8 structures that correspond to endpoints and 18 to branch points. After applying the convolution, the search for feature points corresponds to finding the 26 different filter response values in the resulting signal. We describe how the convolution approach is applied to biometric vein recognition systems and show that our approach yields a 430% speedup when compared to the crossing number approach used in ANSI/NIST.

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