Presentation Attack Detection for Iris Recognition

2018 ◽  
Vol 51 (4) ◽  
pp. 1-35 ◽  
Author(s):  
Adam Czajka ◽  
Kevin W. Bowyer
Keyword(s):  
Iris Recognition ◽  
Attack Detection ◽  
Presentation Attack Detection

scholarly journals Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 1315 ◽  
Author(s):  
Dat Nguyen ◽  
Na Baek ◽  
Tuyen Pham ◽  
Kang Park
Keyword(s):  
Iris Recognition ◽  
Recognition System ◽  
Attack Detection ◽  
Presentation Attack Detection ◽  
Camera Sensor

scholarly journals Deep Learning-Based Enhanced Presentation Attack Detection for Iris Recognition by Combining Features from Local and Global Regions Based on NIR Camera Sensor

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2601 ◽  
Author(s):  
Dat Nguyen ◽  
Tuyen Pham ◽  
Young Lee ◽  
Kang Park
Keyword(s):  
Iris Recognition ◽  
Near Infrared ◽  
Recognition Rate ◽  
Image Features ◽  
Attack Detection ◽  
Support Vector ◽  
Security Level ◽  
Iris Patterns ◽  
Presentation Attack Detection ◽  
Camera Sensor

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.

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