Paper Info
Title
Fingerprint spoof detection using minutiae-based local patches
Abstract
The individuality of fingerprints is being leveraged for a plethora of day-to-day applications, ranging from unlocking a smartphone to international border security. While the primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, the security of the recognition system itself can be jeopardized by spoof attacks. This study addresses the problem of developing accurate and generalizable algorithms for detecting fingerprint spoof attacks. We propose a deep convolutional neural network based approach utilizing local patches extracted around fingerprint minutiae. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state of the art accuracies in fingerprint spoof detection for intra-sensor, cross-material, cross-sensor, as well as cross-dataset testing scenarios. For example, the proposed approach achieves a 69% reduction in average classification error for spoof detection under both known material and cross-material scenarios on LivDet 2015 datasets.
Year
DOI
Venue
2017
10.1109/BTAS.2017.8272745
2017 IEEE International Joint Conference on Biometrics (IJCB)
Keywords
Field
DocType
fingerprint spoof detection,local patches,day-to-day applications,international border security,fingerprint recognition system,reliable user authentication,accurate user authentication,generalizable algorithms,deep convolutional neural network based approach,public-domain LivDet datasets,fingerprint spoof attack,cross-dataset testing scenarios
Economics,Authentication,Pattern recognition,Recognition system,Convolutional neural network,Fingerprint recognition,Minutiae,Fingerprint,Ranging,Artificial intelligence,Finance,Border Security
Conference
ISBN
Citations 
PageRank 
978-1-5386-1125-8
4
0.43
References 
Authors
10
3
Name
Order
Citations
PageRank
Tarang Chugh1455.62
Kai Cao220718.68
Anil Jain3335073334.84