<title>Polarization-encoded fringe-adjusted joint-transform correlator for fingerprint verification systems for high-security applications</title>

FPGA Implementation of MC-CDMA for High Security Applications

CVR Journal of Science & Technology ◽

10.32377/cvrjst0507 ◽

2013 ◽

Vol 5 (1) ◽

pp. 36-41

Author(s):

R. Ganesh ◽

◽

Ch. Sandeep Reddy ◽

Keyword(s):

Fpga Implementation ◽

Security Applications ◽

High Security

Download Full-text

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237218500199 ◽

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.

Download Full-text