diff --git a/doc/index.rst b/doc/index.rst
index c9355ac8776b33d7d603cad9baa4877793ec9993..0fef703937fc291068066e827ef6df565ce6aca4 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -22,6 +22,7 @@ Users Guide
    installation
    baselines
    other_pad_algorithms
+   pulse
    references
    resources
    api
diff --git a/doc/pulse.rst b/doc/pulse.rst
new file mode 100644
index 0000000000000000000000000000000000000000..d832d020db72eb6859d5522eb8870e0531a951f8
--- /dev/null
+++ b/doc/pulse.rst
@@ -0,0 +1,69 @@
+
+.. _bob.pad.face.pulse:
+
+===============
+Pulse-based PAD
+===============
+
+In this section, we briefly describe our work made for face
+presentation attack detection using the blood volume pulse,
+inferred from remote photoplesthymograpy.
+
+The basic idea here is to retrieve the pulse signals from
+face video sequences, to derive features from their frequency
+spectrum and then to learn a classifier to discriminate
+between *bonafide* attempts from presentation attacks.
+
+For this purpose, we describe both :py:class:`bob.bio.base.preprocessor.Preprocessor` and
+:py:class:`bob.bio.base.extractor.Extractor` specifically dedicated to this task.
+
+Preprocessors: Pulse Extraction
+-------------------------------
+
+Preprocessors basically extract pulse signals from face video 
+sequences. They heavily rely on what has been done in `bob.rppg.base`
+so you may want to have a look at `its documentation <https://www.idiap.ch/software/bob/docs/bob/bob.rppg.base/master/index.html>`_. 
+
+In this package, 4 preprocessors have been implemented:
+
+  1. :py:class:`bob.pad.face.preprocessor.LiPulseExtraction` described in [Li_ICPR_2016]_.
+  
+  2. :py:class:`bob.pad.face.preprocessor.Chrom` described in [CHROM]_.
+
+  3. :py:class:`bob.pad.face.preprocessor.SSR` described in [SSR]_.
+
+  4. :py:class:`bob.pad.face.preprocessor.PPGSecure` described in [NOWARA]_.
+
+
+Extractors: Features from Pulses
+--------------------------------
+
+Extractors compute and retrieve features from the pulse signal. All
+implemented extractors act on the frequency spectrum of the pulse signal.
+
+In this package, 3 extractors have been implemented:
+
+  1. :py:class:`bob.pad.face.extractor.LiSpectralFeatures` described in [Li_ICPR_2016]_.
+  
+  2. :py:class:`bob.pad.face.extractor.PPGSecure` described in [NOWARA]_.
+
+  3. :py:class:`bob.pad.face.extractor.LTSS` described in [LTSS]_.
+
+
+
+References
+----------
+
+
+.. [Li_ICPR_2016] *X. Li, J, Komulainen, G. Zhao, P-C Yuen and M. Pietikäinen*
+  **Generalized face anti-spoofing by detecting pulse from face videos**,
+  Intl Conf on Pattern Recognition (ICPR), 2016
+
+.. [CHROM] *de Haan, G. & Jeanne, V*. **Robust Pulse Rate from Chrominance based rPPG**, IEEE Transactions on Biomedical Engineering, 2013. `pdf <http://www.es.ele.tue.nl/~dehaan/pdf/169_ChrominanceBasedPPG.pdf>`__
+
+
+.. [SSR] *Wang, W., Stuijk, S. and de Haan, G*. **A Novel Algorithm for Remote Photoplesthymograpy: Spatial Subspace Rotation**, IEEE Trans. On Biomedical Engineering, 2015
+
+.. [NOWARA] *E. M. Nowara, A. Sabharwal, A. Veeraraghavan*. **PPGSecure: Biometric Presentation Attack Detection Using Photopletysmograms**, IEEE International Conference on Automatic Face & Gesture Recognition, 2017
+
+.. [LTSS] *H .Muckenhirn, P. Korshunov, M. Magimai-Doss, S Marcel*. **Long-Term Spectral Statistics for Voice Presentation Attack Detection**, IEEE Trans. On Audio, Speech and Language Processing, 2017