scholarly journals Performance evaluation of fingerprint verification systems

2006 ◽  
Vol 28 (1) ◽  
pp. 3-18 ◽  
Author(s):  
R. Cappelli ◽  
D. Maio ◽  
D. Maltoni ◽  
J.L. Wayman ◽  
A.K. Jain
Keyword(s):  
Performance Evaluation ◽  
Fingerprint Verification ◽  
Verification Systems

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

2018 ◽  
Vol 30 (03) ◽  
pp. 1850019
Author(s):  
Fatemeh Alimardani ◽  
Reza Boostani
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
Acceptance Rate ◽  
Feature Reduction ◽  
Support Vector ◽  
Fingerprint Verification ◽  
Linear Discriminant ◽  
Comparative Results ◽  
Verification Systems

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

An evaluation of indirect attacks and countermeasures in fingerprint verification systems

2011 ◽  
Vol 32 (12) ◽  
pp. 1643-1651 ◽  
Author(s):  
Marcos Martinez-Diaz ◽  
Julian Fierrez ◽  
Javier Galbally ◽  
Javier Ortega-Garcia
Keyword(s):  
Fingerprint Verification ◽  
Verification Systems

scholarly journals Cross-Sensor Fingerprint Matching Using Siamese Network and Adversarial Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3657
Author(s):  
Adhwa Alrashidi ◽  
Ashwaq Alotaibi ◽  
Muhammad Hussain ◽  
Helala AlShehri ◽  
Hatim A. AboAlSamh ◽  
...  
Keyword(s):  
State Of The Art ◽  
Fingerprint Matching ◽  
Fingerprint Verification ◽  
Adversarial Learning ◽  
Siamese Network ◽  
Comparable Performance ◽  
Public Datasets ◽  
Verification Systems ◽  
Verification Techniques ◽  
Cross Matching

The fingerprint is one of the leading biometric modalities that is used worldwide for authenticating the identity of persons. Over time, a lot of research has been conducted to develop automatic fingerprint verification techniques. However, due to different authentication needs, the use of different sensors and the fingerprint verification systems encounter cross-sensor matching or sensor interoperability challenges, where different sensors are used for the enrollment and query phases. The challenge is to develop an efficient, robust and automatic system for cross-sensor matching. This paper proposes a new cross-matching system (SiameseFinger) using the Siamese network that takes the features extracted using the Gabor-HoG descriptor. The proposed Siamese network is trained using adversarial learning. The SiameseFinger was evaluated on two benchmark public datasets FingerPass and MOLF. The results of the experiments presented in this paper indicate that SiameseFinger achieves a comparable performance with that of the state-of-the-art methods.

Evaluation of direct attacks to fingerprint verification systems

2010 ◽  
Vol 47 (3-4) ◽  
pp. 243-254 ◽  
Author(s):  
J. Galbally ◽  
J. Fierrez ◽  
F. Alonso-Fernandez ◽  
M. Martinez-Diaz
Keyword(s):  
Fingerprint Verification ◽  
Verification Systems
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