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

 
: Fang, Meiling; Damer, Naser; Boutros, Fadi; Kirchbuchner, Florian; Kuijper, Arjan

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International Society of Information Fusion -ISIF-:
FUSION 2020, 23rd International Conference on Information Fusion
Piscataway, NJ: IEEE, 2020
ISBN: 978-0-578-64709-8
ISBN: 978-1-7281-6830-2
8 S.
International Conference on Information Fusion (FUSION) <23, 2020, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
ATHENE
Englisch
Konferenzbeitrag
Fraunhofer IGD ()
Lead Topic- Smart City; Lead Topic- Visual Computing as a Service; Research Line- Computer vision (CV); Research Line- Human computer interaction (HCI); Biometrics; Spoofing attacks; Deep learning; Iris recognition; Information fusion; ATHENE; CRISP

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).

: http://publica.fraunhofer.de/dokumente/N-602345.html