Minutiae Matching Based Fingerprint Verification Using Delaunay Triangulation and Aligned-Edge-Guided Triangle Matching

2005 ◽  
pp. 270-278 ◽  
Author(s):  
Huimin Deng ◽  
Qiang Huo
Keyword(s):  
Delaunay Triangulation ◽  
Fingerprint Verification ◽  
Minutiae Matching

scholarly journals Image enhancement and minutiae matching in fingerprint verification

2003 ◽  
Vol 24 (9-10) ◽  
pp. 1349-1360 ◽  
Author(s):  
Yuliang He ◽  
Jie Tian ◽  
Xiping Luo ◽  
Tanghui Zhang
Keyword(s):  
Image Enhancement ◽  
Fingerprint Verification ◽  
Minutiae Matching

A Secured Contactless Fingerprint Verification Method Using a Minutiae Matching Technique

2018 ◽  
pp. 221-245
Author(s):  
Tahirou Djara ◽  
Marc Kokou Assogba ◽  
Antoine Vianou
Keyword(s):  
Orientation Angle ◽  
Input Image ◽  
Fingerprint Matching ◽  
Fingerprint Verification ◽  
Excessive Pressure ◽  
Verification Method ◽  
Template Protection ◽  
Euclidean Distances ◽  
Minutiae Matching ◽  
Future Work

Most matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising. Future work will enhance the proposed verification method by a new template protection technique.

scholarly journals FINGERPRINT VERIFICATION BY USING MINUTIAE MATCHING TECHNIQUE

2020 ◽  
Vol 2 (10) ◽  
Author(s):  
BAPPA SARKAR ◽  
JOYASSREE SEN ◽  
MD. ATIQUR RAHMAN ◽  
MD. HABIBUR RAHMAN
Keyword(s):  
Fingerprint Verification ◽  
Matching Technique ◽  
Minutiae Matching

scholarly journals Fingerprint verification using computational geometry

DYNA ◽  
2016 ◽  
Vol 83 (195) ◽  
pp. 128-137 ◽  
Author(s):  
Manuel Ramírez Flores ◽  
Gualberto Aguilar Torres ◽  
Gina Gallegos García ◽  
Miguel Ángel García Licona
Keyword(s):  
Delaunay Triangulation ◽  
Error Rate ◽  
Connected Graph ◽  
Local Stability ◽  
Acceptance Rate ◽  
Equal Error Rate ◽  
False Acceptance Rate ◽  
Fingerprint Verification ◽  
Minimum Angle ◽  
False Acceptance

This paper presents a robust minutiae based method for fingerprint verification. The proposed method uses Delaunay Triangulation to represent minutiae as nodes of a connected graph composed of triangles. The minimum angle over all triangulations is maximized, which gives local stability to the constructed structures against rotation and translation variations. Geometric thresholds and minutiae data were used to characterize the triangulations created from input and template fingerprint images. The effectiveness of the proposed method is confirmed through calculations of false acceptance rate (FAR), false rejected rate (FRR) and equal error rate (EER) over FVC2002 databases compared to the results of other approaches.

A Contactless Fingerprint Verification Method using a Minutiae Matching Technique

2016 ◽  
Vol 6 (1) ◽  
pp. 12-27 ◽  
Author(s):  
Tahirou Djara ◽  
Marc Kokou Assogba ◽  
Antoine Vianou
Keyword(s):  
Orientation Angle ◽  
Input Image ◽  
Fingerprint Matching ◽  
Matching Method ◽  
Fingerprint Verification ◽  
Excessive Pressure ◽  
Verification Method ◽  
Matching Technique ◽  
Euclidean Distances ◽  
Minutiae Matching

Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.

Dynamic Strip Partitioning Method Oriented Parallel Computing for Construction of Delaunay Triangulation

2012 ◽  
Vol 14 (1) ◽  
pp. 55-61 ◽  
Author(s):  
Lin QI ◽  
Jie SHEN ◽  
Lishuai GUO ◽  
Tong ZHOU
Keyword(s):  
Parallel Computing ◽  
Delaunay Triangulation ◽  
Oriented Parallel
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