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To Detect or not to Detect: The Right Faces to Morph

Damer, Naser and Saladie, Alexandra Mosegui and Zienert, Steffen and Wainakh, Yaza and Terhorst, Philipp and Kirchbuchner, Florian and Kuijper, Arjan (2019):
To Detect or not to Detect: The Right Faces to Morph.
pp. 1-8, 12th IAPR International Conference On Biometrics (ICB 2019), Crete, Greece, 04.-07. June, 2019, DOI: 10.1109/ICB45273.2019.8987316,
[Conference or Workshop Item]

Abstract

Recent works have studied the face morphing attack detection performance generalization over variations in morphing approaches, image re-digitization, and image source variations. However, these works assumed a constant approach for selecting the images to be morphed (pairing) across their training and testing data. A realistic variation in the pairing protocol in the training data can result in challenges and opportunities for a stable attack detector. This work extensively study this issue by building a novel database with three different pairing protocols and two different morphing approaches. We study the detection generalization over these variations for single image and differential attack detection, along with handcrafted and CNNbased features. Our observations included that training an attack detection solution on attacks created from dissimilar face images, in contrary to the common practice, can result in an overall more generalized detection performance. Moreover, we found that differential attack detection is very sensitive to variations in morphing and pairing protocols.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Damer, Naser and Saladie, Alexandra Mosegui and Zienert, Steffen and Wainakh, Yaza and Terhorst, Philipp and Kirchbuchner, Florian and Kuijper, Arjan
Title: To Detect or not to Detect: The Right Faces to Morph
Language: English
Abstract:

Recent works have studied the face morphing attack detection performance generalization over variations in morphing approaches, image re-digitization, and image source variations. However, these works assumed a constant approach for selecting the images to be morphed (pairing) across their training and testing data. A realistic variation in the pairing protocol in the training data can result in challenges and opportunities for a stable attack detector. This work extensively study this issue by building a novel database with three different pairing protocols and two different morphing approaches. We study the detection generalization over these variations for single image and differential attack detection, along with handcrafted and CNNbased features. Our observations included that training an attack detection solution on attacks created from dissimilar face images, in contrary to the common practice, can result in an overall more generalized detection performance. Moreover, we found that differential attack detection is very sensitive to variations in morphing and pairing protocols.

Uncontrolled Keywords: Biometrics Spoofing attacks Face recognition
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: 12th IAPR International Conference On Biometrics (ICB 2019)
Event Location: Crete, Greece
Event Dates: 04.-07. June, 2019
Date Deposited: 14 Apr 2020 07:35
DOI: 10.1109/ICB45273.2019.8987316
Official URL: https://doi.org/10.1109/ICB45273.2019.8987316
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