Details

Autor(en) / Beteiligte

• Atoum, Yousef
• Yaojie Liu
• Jourabloo, Amin
• Xiaoming Liu

Titel

Face anti-spoofing using patch and depth-based CNNs

Ist Teil von

• 2017 IEEE International Joint Conference on Biometrics (IJCB), 2017, p.319-328

Ort / Verlag

IEEE

Erscheinungsjahr

2017

Quelle

IEL

Beschreibungen/Notizen

• The face image is the most accessible biometric modality which is used for highly accurate face recognition systems, while it is vulnerable to many different types of presentation attacks. Face anti-spoofing is a very critical step before feeding the face image to biometric systems. In this paper, we propose a novel two-stream CNN-based approach for face anti-spoofing, by extracting the local features and holistic depth maps from the face images. The local features facilitate CNN to discriminate the spoof patches independent of the spatial face areas. On the other hand, holistic depth map examine whether the input image has a face-like depth. Extensive experiments are conducted on the challenging databases (CASIA-FASD, MSU-USSA, and Replay Attack), with comparison to the state of the art.