Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

Dat Nguyen; Na Baek; Tuyen Pham; Kang Park

doi:10.3390/s18051315

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

Sensors ◽

10.3390/s18051315 ◽

2018 ◽

Vol 18 (5) ◽

pp. 1315 ◽

Cited By ~ 10

Author(s):

Dat Nguyen ◽

Na Baek ◽

Tuyen Pham ◽

Kang Park

Keyword(s):

Iris Recognition ◽

Recognition System ◽

Attack Detection ◽

Presentation Attack Detection ◽

Camera Sensor

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Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information

Sensors ◽

10.3390/s19020410 ◽

2019 ◽

Vol 19 (2) ◽

pp. 410 ◽

Cited By ~ 5

Author(s):

Dat Nguyen ◽

Tuyen Pham ◽

Min Lee ◽

Kang Park

Keyword(s):

Neural Network ◽

Visible Light ◽

Spatial Information ◽

Recognition System ◽

Image Features ◽

Attack Detection ◽

Temporal Information ◽

Public Datasets ◽

Presentation Attack Detection ◽

Camera Sensor

Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

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

Sensors ◽

10.3390/s18082601 ◽

2018 ◽

Vol 18 (8) ◽

pp. 2601 ◽

Cited By ~ 13

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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