Paper Info
Title
A DCNN Based Fingerprint Liveness Detection Algorithm with Voting Strategy.
Abstract
The concern of the safety of fingerprint authentication system is rising with its widely using for it is easy to be attacked by spoof (fake) fingerprints. Fake fingerprints are usually made of Ploy-Doh, silicon or other artifacts. So most current approaches rely on fingerprint liveness detection as main anti-spoofing mechanisms. Recently, researchers propose to use local feature descriptor for fingerprint liveness detection, but the results are still not satisfying the real world application requirement. Inspired by the newly trend of application of Deep Convolution Neural Network (DCNN) in computer vision field and its outstanding performance in face detection and image classification, we propose a novel fingerprint liveness detection method based on DCNN and voting strategy, which performs better than handcraft feature and optimize the process of feature extraction and classifier training simultaneously. The experimental results on the datasets of LivDet2011 and LivDet2013 show that the proposed algorithm has great improvement compare to the former state-of-theart algorithm, and keep highly real-time performance at the same time.
Year
DOI
Venue
2015
10.1007/978-3-319-25417-3_29
BIOMETRIC RECOGNITION, CCBR 2015
Keywords
Field
DocType
Fingerprint liveness detection,Fingerprint anti-spoofing,Deep learning,Deep convolution neural network,Voting strategy
Convolutional neural network,Fingerprint recognition,Computer science,Algorithm,Feature extraction,Fingerprint,Artificial intelligence,Face detection,Deep learning,Contextual image classification,Liveness
Conference
Volume
ISSN
Citations 
9428
0302-9743
16
PageRank 
References 
Authors
0.67
12
4
Name
Order
Citations
PageRank
Chenggang Wang1160.67
Ke Li2211.47
Zhihong Wu3161.01
Qijun Zhao441938.37