Security and Reliability Assessment for Biometric Systems

2013 ◽  
pp. 3-32
Author(s):  
GAYATRI MIRAJKAR
Keyword(s):  
Reliability Assessment ◽  
Biometric Systems ◽  
Security And Reliability

scholarly journals Unified Generative Adversarial Networks for Multidomain Fingerprint Presentation Attack Detection

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1089
Author(s):  
Soha B. Sandouka ◽  
Yakoub Bazi ◽  
Haikel Alhichri ◽  
Naif Alajlan
Keyword(s):  
Attack Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
The Public ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Biometric Systems ◽  
Security And Reliability ◽  
Presentation Attack Detection

With the rapid growth of fingerprint-based biometric systems, it is essential to ensure the security and reliability of the deployed algorithms. Indeed, the security vulnerability of these systems has been widely recognized. Thus, it is critical to enhance the generalization ability of fingerprint presentation attack detection (PAD) cross-sensor and cross-material settings. In this work, we propose a novel solution for addressing the case of a single source domain (sensor) with large labeled real/fake fingerprint images and multiple target domains (sensors) with only few real images obtained from different sensors. Our aim is to build a model that leverages the limited sample issues in all target domains by transferring knowledge from the source domain. To this end, we train a unified generative adversarial network (UGAN) for multidomain conversion to learn several mappings between all domains. This allows us to generate additional synthetic images for the target domains from the source domain to reduce the distribution shift between fingerprint representations. Then, we train a scale compound network (EfficientNetV2) coupled with multiple head classifiers (one classifier for each domain) using the source domain and the translated images. The outputs of these classifiers are then aggregated using an additional fusion layer with learnable weights. In the experiments, we validate the proposed methodology on the public LivDet2015 dataset. The experimental results show that the proposed method improves the average classification accuracy over twelve classification scenarios from 67.80 to 80.44% after adaptation.

Reliability Assessment for Load Bearing Underframe on Car Body

2014 ◽  
Vol 633-634 ◽  
pp. 1206-1212
Author(s):  
Ai Qin Tian ◽  
Hao Dong
Keyword(s):  
Numerical Simulation ◽  
Brittle Failure ◽  
Reliability Assessment ◽  
Crack Size ◽  
Security Assessment ◽  
Surface Cracks ◽  
Defect Tolerance ◽  
Critical Crack ◽  
Car Body ◽  
Security And Reliability

The security assessment of large underframe on car body is proposed through the SINTAP/FITNET method and with the help of numerical simulation technology of finite element based on some EMU powered car in service at present. The security and reliability of the underframe structure is predicted. The defect tolerance that the underframe is ‘fitness for service’ is put forward in this paper. The result shows that the crack shape a/c has little impact on the security with the semielliptic surface cracks assumption. In addition, the security assessment on dangerous areas and critical crack sizes are acquired based on the assumption a/c=0.2. The critical crack sizes of the dangerous area 1, area 2 and area 3 are about 5 to 5.5 millimeters, while the unilateral penetrate crack size of the area 4 reaches 40 millimeters. The structures are suit for service as the risk of brittle failure is extremely small.

Test and Research Development of Electric Cable toward to Security and Reliability

2012 ◽  
Vol 132 (4) ◽  
pp. 200-203
Author(s):  
Masayuki NAGAO ◽  
Muneaki KURIMOTO ◽  
Risyun KIN ◽  
Tomohiro KAWASHIMA ◽  
Yoshinobu MURAKAMI
Keyword(s):  
Research Development ◽  
Electric Cable ◽  
Security And Reliability

Human Ear Pattern Recognition System

2017 ◽  
Vol 7 (8) ◽  
pp. 117 ◽  
Author(s):  
V. Jagan Naveen ◽  
K. Krishna Kishore ◽  
P. Rajesh Kumar
Keyword(s):  
Pattern Recognition ◽  
Expectation Maximization Algorithm ◽  
Recognition System ◽  
Modern World ◽  
Person Identification ◽  
Biometric Systems ◽  
Different Types ◽  
Recognition Systems ◽  
Human Ear ◽  
Pca Algorithm

In the modern world, human recognition systems play an important role to   improve security by reducing chances of evasion. Human ear is used for person identification .In the Empirical study on research on human ear, 10000 images are taken to find the uniqueness of the ear. Ear based system is one of the few biometric systems which can provides stable characteristics over the age. In this paper, ear images are taken from mathematical analysis of images (AMI) ear data base and the analysis is done on ear pattern recognition based on the Expectation maximization algorithm and k means algorithm.  Pattern of ears affected with different types of noises are recognized based on Principle component analysis (PCA) algorithm.

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