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

 
: Damer, Naser; Saladie, Alexandra Moseguí; Zienert, Steffen; Wainakh, Yaza; Kirchbuchner, Florian; Kuijper, Arjan; Terhörst, Philipp

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Institute of Electrical and Electronics Engineers -IEEE-; IEEE Computer Society; International Association for Pattern Recognition -IAPR-:
12th IAPR International Conference on Biometrics, ICB 2019 : 4-7 June 2019, Crete, Greece
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-3640-0
ISBN: 978-1-7281-3641-7
8 pp.
International Conference on Biometrics (ICB) <12, 2019, Crete>
English
Conference Paper
Fraunhofer IGD ()
CRISP; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); biometrics; spoofing attacks; face recognition

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 CNN-based 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.

: http://publica.fraunhofer.de/documents/N-577805.html