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Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection

Fang, Meiling and Damer, Naser and Boutros, Fadi and Kirchbuchner, Florian and Kuijper, Arjan (2020):
Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection.
pp. 1-8, IEEE, 23rd International Conference on Information Fusion (FUSION 2020), virtual Conference, 06.-09.07., ISBN 978-1-7281-6830-2,
DOI: 10.23919/FUSION45008.2020.9190424,
[Conference or Workshop Item]

Abstract

Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-theshelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).

Item Type: Conference or Workshop Item
Erschienen: 2020
Creators: Fang, Meiling and Damer, Naser and Boutros, Fadi and Kirchbuchner, Florian and Kuijper, Arjan
Title: Deep Learning Multi-layer Fusion for an Accurate Iris Presentation Attack Detection
Language: English
Abstract:

Iris presentation attack detection (PAD) algorithms are developed to address the vulnerability of iris recognition systems to presentation attacks. Taking into account that the deep features successfully improved computer vision performance in various fields including iris recognition, it is natural to use features extracted from deep neural networks for iris PAD. Each layer in a deep learning network carries features of different level of abstraction. The features extracted from the first layer to the higher layers become more complex and more abstract. This might point our complementary information in these features that can collaborate towards an accurate PAD decision. Therefore, we propose an iris PAD solution based on multi-layer fusion. The information extracted from the last several convolutional layers are fused on two levels, feature-level and score-level. We demonstrated experiments on both, off-theshelf pre-trained network and network trained from scratch. An extensive experiment also explores the complementary between different layer combinations of deep features. Our experimental results show that feature-level based multi-layer fusion method performs better than the best single layer feature extractor in most cases. In addition, our fusion results achieve similar or better results than the state-of-the-art algorithms on the Notre Dame and IIITD-WVU databases of the Iris Liveness Detection Competition 2017 (LivDet-Iris 2017).

Publisher: IEEE
ISBN: 978-1-7281-6830-2
Uncontrolled Keywords: Biometrics, Spoofing attacks, Deep learning, Iris recognition, Information fusion
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: 23rd International Conference on Information Fusion (FUSION 2020)
Event Location: virtual Conference
Event Dates: 06.-09.07.
Date Deposited: 22 Sep 2020 14:12
DOI: 10.23919/FUSION45008.2020.9190424
Official URL: https://ieeexplore.ieee.org/document/9190424
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