EfficientNet Combined with Generative Adversarial Networks for Presentation Attack Detection

被引：1
|
作者：
Sandouka, Soha B. [1 ]
Bazi, Yakoub [1 ]
Al Rahhal, Mohamad Mahmoud [2 ]
机构：
[1] King Saud Univ, Comp Engn Dept, Riyadh, Saudi Arabia
[2] King Saud Univ, Appl Comp Sci Dept, Riyadh, Saudi Arabia
来源：
2020 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE & MODERN ASSISTIVE TECHNOLOGY (ICAIMAT) | 2020年
关键词：
Fingerprint presentation attack detection; liveness detection; deep learning; Convolutional Neural Networks (CNN); generalization; Generative Adversarial Network (GANs);
D O I：
10.1109/icaimat51101.2020.9308017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
in recent years, fingerprint-based biometric systems have grown rapidly as they are used for various applications such as mobile payments, international border security, and financial transactions. Although the widespread of these systems, it has been found that they are vulnerable to presentation attacks (i.e., spoof attacks). Therefore, improving the generalization ability of fingerprint PAD over unknown materials and unknown sensors is of primary importance. In this work, we proposed a fingerprint PAD with improved cross-sensor and cross-material generalization based on state-of-the-art CNN network; i.e., EfficientNet combined with Generative Adversarial Network (GANs). We will validate the proposed methodologies on the public LivDet2015 dataset provided by the liveness detection competition.
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页数：5
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