%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{George_Idiap-RR-12-2020,
         author = {George, Anjith and Marcel, S{\'{e}}bastien},
       projects = {Idiap, ODIN/BATL},
          month = {6},
          title = {Can Your Face Detector Do Anti-spoofing? Face Presentation Attack Detection with a Multi-Channel Face Detector},
           type = {Idiap-RR},
         number = {Idiap-RR-12-2020},
           year = {2020},
    institution = {Idiap},
       abstract = {In a typical face recognition pipeline, the task of
the face detector is to localize the face region. However, the face
detector localizes regions that look like a face, irrespective of the
liveliness of the face, which makes the entire system susceptible
to presentation attacks. In this work, we try to reformulate the
task of the face detector to detect real faces, thus eliminating
the threat of presentation attacks. While this task could be
challenging with visible spectrum images alone, we leverage the
multi-channel information available from off the shelf devices
(such as color, depth, and infrared channels) to design a multi-
channel face detector. The proposed system can be used as a
live-face detector obviating the need for a separate presentation
attack detection module, making the system reliable in practice
without any additional computational overhead. The main idea
is to leverage a single-stage object detection framework, with
a joint representation obtained from different channels for the
PAD task. We have evaluated our approach in the multi-channel
WMCA dataset containing a wide variety of attacks to show the
effectiveness of the proposed framework.},
            pdf = {https://publications.idiap.ch/attachments/reports/2019/George_Idiap-RR-12-2020.pdf}
}