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@INCOLLECTION{Heusch_SPRINGER_2019,
         author = {Heusch, Guillaume and Marcel, S{\'{e}}bastien},
         editor = {Marcel, S{\'{e}}bastien and Nixon, Mark and Fierrez, Julian and Evans, Nicholas},
       projects = {ODIN/BATL},
          month = apr,
          title = {Remote Blood Pulse Analysis for Face Presentation Attack Detection},
      booktitle = {Handbook of Biometric Anti-Spoofing},
        edition = {2nd},
         series = {Advances in Computer Vision and Pattern Recognition},
        chapter = {10},
           year = {2019},
      publisher = {Springer},
           isbn = {978-3-319-92627-8},
            url = {https://www.springer.com/us/book/9783319926261},
       abstract = {In this chapter, the usage of Remote Photoplethysmography (rPPG\index{Remote Photoplethysmography (rPPG)}) as a mean for
face presentation attack detection is investigated. Remote
photoplethysmography consists in retrieving the heart-rate of a subject from a
video sequence containing some skin, and recorded at a distance. To get a
pulse signal, such methods take advantage of subtle color variation on skin
pixels due to the blood flowing through vessels.  Since the inferred pulse
signal gives information on the liveness of the recorded subject, it can be
used for biometric presentation attack detection (PAD\index{Presentation Attack Detection (PAD)}).  Inspired by work made
for speaker presentation attack detection, we propose to use long-term
spectral statistical features of the pulse signal to discriminate real
accesses from attack attempts. A thorough experimental evaluation, with
different rPPG and classification algorithms is carried on four publicly
available datasets containing a wide range of face presentation attacks.
Obtained results suggest that the proposed features are effective for this
task, and we empirically show that our approach performs better than
state-of-the-art rPPG-based presentation attack detection algorithms.}
}