NDLI: Fingerprint liveness map construction using convolutional neural network

Content Provider	IET Digital Library
Author	Jung, H. Y. Heo, Y. S.
Abstract	With increasing markets for fingerprint authentication, there are also increasing concerns about spoofs or synthetically produced fingerprint identifications that can bypass the authentication process. In this Letter, the authors introduce a new convolutional neural networks (CNNs) architecture for fingerprint liveness detection problem that can provide a more robust framework for network training and detection than previous methods. The proposed method employs squared regression error for each receptive field without the usage of the fully connected layer. Such structure provides following advantages from the previous liveness fingerprint CNN. First, unlike the previous techniques which rely on the pre-trained features, the proposed CNN can be trained directly from fingerprints as the loss is minimised for each receptive field. Second, in contrast to the cross-entropy layer, the squared error layer allows them to set up a threshold value that can control the acceptable level of false positives or false negatives. Third, the absence of a fully connected layer allows them to crop the input fingerprints such that a trade-off between accuracy and computation time can be made without the negative effects of re-scaling. The proposed CNN is shown to provide higher accuracy for three out of four datasets when evaluated against the state-of-the-art method.
Starting Page	564
Ending Page	566
Page Count	3
ISSN	00135194
Volume Number	54
e-ISSN	1350911X
Issue Number	Issue 9, May (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/el/54/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/el.2018.0621
Journal	Electronics Letters
Publisher Date	2018-03-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	CNN Architecture Computer Vision And Image Processing Technique Convolutional Neural Network Data Security Fingerprint Authentication Fingerprint Identification Fingerprint Identifications Fingerprint Liveness Detection Problem Fingerprint Liveness Map Construction Fully Connected Layer Image Recognition Learning in AI Loss Minimization Network Training Neural Computing Technique Neural Net Architecture Object Detection Receptive Field Regression Analysis Squared Regression Error Statistics
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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