A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy

2015 ◽  
pp. 241-249 ◽  
Author(s):  
Chenggang Wang ◽  
Ke Li ◽  
Zhihong Wu ◽  
Qijun Zhao
Keyword(s):  
Detection Algorithm ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Voting Strategy

Fingerprint liveness detection using an improved CNN with the spatial pyramid pooling structure

2020 ◽  
Author(s):  
Chengsheng Yuan ◽  
Qi Cui ◽  
Xingming Sun ◽  
Q.M. Jonathan Wu ◽  
Sheng Wu
Keyword(s):  
Liveness Detection ◽  
Spatial Pyramid Pooling ◽  
Fingerprint Liveness Detection ◽  
Spatial Pyramid

Fingerprint liveness detection using multiscale difference co-occurrence matrix

2016 ◽  
Vol 55 (6) ◽  
pp. 063111 ◽  
Author(s):  
Chengsheng Yuan ◽  
Zhihua Xia ◽  
Xingming Sun ◽  
Decai Sun ◽  
Rui Lv
Keyword(s):  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Occurrence Matrix

Fingerprint Presentation Attack Detection Using Transfer Learning Approach

2021 ◽  
Vol 17 (1) ◽  
pp. 53-67
Author(s):  
Rajneesh Rani ◽  
Harpreet Singh
Keyword(s):  
Transfer Learning ◽  
Attack Detection ◽  
Classification Error ◽  
Learning Approach ◽  
Biometric Authentication ◽  
The Past ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Software And Hardware ◽  
Presentation Attack Detection

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

One-Class SVM with Negative Examples for Fingerprint Liveness Detection

2014 ◽  
pp. 216-224
Author(s):  
Xiaofei Jia ◽  
Yali Zang ◽  
Ning Zhang ◽  
Xin Yang ◽  
Jie Tian
Keyword(s):  
Liveness Detection ◽  
Fingerprint Liveness Detection

scholarly journals A Score-Level Fusion of Fingerprint Matching With Fingerprint Liveness Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 183391-183400
Author(s):  
Yongliang Zhang ◽  
Chenhao Gao ◽  
Shengyi Pan ◽  
Zhiwei Li ◽  
Yuanyang Xu ◽  
...  
Keyword(s):  
Fingerprint Matching ◽  
Score Level Fusion ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Level Fusion
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