Paper Info
Title
Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor.
Abstract
Iris recognition systems have been used in high-security-level applications because of their high recognition rate and the distinctiveness of iris patterns. However, as reported by recent studies, an iris recognition system can be fooled by the use of artificial iris patterns and lead to a reduction in its security level. The accuracies of previous presentation attack detection research are limited because they used only features extracted from global iris region image. To overcome this problem, we propose a new presentation attack detection method for iris recognition by combining features extracted from both local and global iris regions, using convolutional neural networks and support vector machines based on a near-infrared (NIR) light camera sensor. The detection results using each kind of image features are fused, based on two fusion methods of feature level and score level to enhance the detection ability of each kind of image features. Through extensive experiments using two popular public datasets (LivDet-Iris-2017 Warsaw and Notre Dame Contact Lens Detection 2015) and their fusion, we validate the efficiency of our proposed method by providing smaller detection errors than those produced by previous studies.
Year
DOI
Venue
2018
10.3390/s18082601
SENSORS
Keywords
Field
DocType
iris recognition,presentation attack detection,deep learning,support vector machines,NIR camera sensor
Computer vision,Iris recognition,Image sensor,Electronic engineering,Artificial intelligence,Engineering,Deep learning
Journal
Volume
Issue
Citations 
18
8.0
0
PageRank 
References 
Authors
0.34
32
4
Name
Order
Citations
PageRank
Dat Tien Nguyen120824.00
Tuyen Danh Pham2709.84
Young-Woo Lee323.11
Kang Ryoung Park41325104.82