[extractor] added feature extractor from Li paper

bob/pad/face/extractor/FreqFeatures.py

+#!/usr/bin/env python
+# encoding: utf-8
+
+import numpy
+
+from bob.bio.base.extractor import Extractor
+
+import logging
+logger = logging.getLogger("bob.pad.face")
+
+from scipy.signal import welch
+
+
+class FreqFeatures(Extractor, object):
+  """
+  Compute features from pulse signals in the three color channels.
+
+  The features are described in the following article:
+  
+    @InProceedings{li-icpr-2016,
+    Author         = {Li, X. and Komulainen, J. and Zhao, G. and Yuen, P-C.
+                     and Pietik\"ainen, M.},
+    Title          = {Generalized {F}ace {A}nti-spoofing by {D}etecting
+                     {P}ulse {F}rom {F}ace {V}ideos},
+    BookTitle      = {Intl {C}onf. on {P}attern {R}ecognition ({ICPR})},
+    Pages          = {4244-4249},
+    year           = 2016
+    }
+
+
+  **Parameters:**
+
+  framerate: int
+    The sampling frequency of the signal (i.e the framerate ...) 
+
+  nsegments: int
+    Number of overlapping segments in Welch procedure
+
+  nfft: int
+    Number of points to compute the FFT
+
+  debug: boolean
+    Plot stuff
+  """
+  def __init__(self, framerate=25, nsegments=12, nfft=128, debug=False, **kwargs):
+
+    super(FreqFeatures, self).__init__()
+    
+    self.framerate = framerate
+    self.nsegments = nsegments
+    self.nfft = nfft
+    self.debug = debug
+
+  def __call__(self, signal):
+    """
+    Compute the frequency spectrum for the given signal.
+
+    **Parameters:**
+
+    signal: numpy.array 
+      The signal
+
+    **Returns:**
+
+      freq: numpy.array 
+       the frequency spectrum 
+    """
+    assert signal.ndim == 2 and signal.shape[1] == 3, "You should provide 3 pulse signals"
+   
+    feature = numpy.zeros(6)
+
+    spectrum_dim = int((self.nfft / 2) + 1)
+    psds = numpy.zeros((3, spectrum_dim))
+    for i in range(3):
+      f, psds[i] = welch(signal[:, i], self.framerate, nperseg=self.nsegments, nfft=self.nfft)
+
+    # find the max of the frequency spectrum in the range of interest
+    first = numpy.where(f > 0.7)[0]
+    last = numpy.where(f < 4)[0]
+    first_index = first[0]
+    last_index = last[-1]
+    range_of_interest = range(first_index, last_index + 1, 1)
+    
+    for i in range(3):
+      total_power = numpy.sum(psds[i, range_of_interest])
+      max_power = numpy.max(psds[i, range_of_interest])
+      feature[i] = max_power
+      feature[i+3] = max_power / total_power
+      print (max_power)
+      print (max_power / total_power)
+
+      if self.debug:
+        f_max = f[range_of_interest[max_idx]]
+        max_idx = numpy.argmax(psds[i, range_of_interest])
+        from matplotlib import pyplot
+        pyplot.semilogy(f, psds[i], 'k')
+        xmax, xmin, ymax, ymin = pyplot.axis()
+        pyplot.vlines(f[range_of_interest[max_idx]], ymin, ymax, color='red')
+        pyplot.title('Power spectrum of the signal')
+        pyplot.show()
+
+    print(feature)
+    import sys
+    sys.exit()
+    return feature
+

bob/pad/face/extractor/__init__.py

@@ -6,6 +6,7 @@ from .VideoQualityMeasure import VideoQualityMeasure
 from .FrameDiffFeatures import FrameDiffFeatures
 
 from .FrequencySpectrum import FrequencySpectrum
+from .FreqFeatures import FreqFeatures
 
 def __appropriate__(*args):
     """Says object was actually declared here, and not in the import module.