Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Face Liveness Detection Competition (LivDet-Face) - 2021

 
: Purnapatra, Sandip; Smalt, Nic; Bahmani, Keivan; Das, Priyanka; Yambay, David; Mohammadi, Amir; George, Anjith; Bourlai, Thirimachos; Marcel, Sébastien; Schuckers, Stephanie; Fang, Meiling; Damer, Naser; Boutros, Fadi; Kuijper, Arjan; Kantarci, Alperen; Demir, Başar; Yildiz, Zafer; Ghafoory, Zabi; Dertli, Hasan; Ekenel, Hazım Kemal; Vu, Son; Christophides, Vassilis; Dashuang, Liang; Guanghao, Zhang; Zhanlong, Hao; Junfu, Liu; Yufeng, Jin; Liu, Samo; Huang, Samuel; Kuei, Salieri; Singh, Jag Mohan; Ramachandra, Raghavendra

:

Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Biometrics Council; International Association for Pattern Recognition -IAPR-:
IEEE International Joint Conference on Biometrics, IJCB 2021 : 4-7 August 2021, Shenzhen, China, virtual
Piscataway, NJ: IEEE, 2021
ISBN: 978-1-6654-3781-3
ISBN: 978-1-6654-3780-6
Art. 9484359, 10 pp.
International Joint Conference on Biometrics (IJCB) <2021, Online>
Bundesministerium für Bildung und Forschung BMBF (Deutschland)
ATHENE
English
Conference Paper
Fraunhofer IGD ()
Lead Topic: Digitized Work; Lead Topic: Smart City; Research Line: Computer vision (CV); Research Line- Machine Learning (ML); biometrics; deep learning; machine learning; face recognition; spoofing attacks; ATHENE; CRISP

Abstract
Liveness Detection (LivDet)-Face is an international competition series open to academia and industry. The competition’s objective is to assess and report state-of-the-art in liveness / Presentation Attack Detection (PAD) for face recognition. Impersonation and presentation of false samples to the sensors can be classified as presentation attacks and the ability for the sensors to detect such attempts is known as PAD. LivDet-Face 2021 * will be the first edition of the face liveness competition. This competition serves as an important benchmark in face presentation attack detection, offering (a) an independent assessment of the current state of the art in face PAD, and (b) a common evaluation protocol, availability of Presentation Attack Instruments (PAI) and live face image dataset through the Biometric Evaluation and Testing (BEAT) platform. The competition can be easily followed by researchers after it is closed, in a platform in which participants can compare their solutions against the LivDet-Face winners.

: http://publica.fraunhofer.de/documents/N-638513.html