Following the discussion here: https://groups.google.com/forum/#!topic/bob-devel/EIp1nvw5-vQ

Looks like we have a corner case where the scores have a very large peak in their distribution: The scores are also available: fusion_all_200_datatset.npy To load them:

>>> scores  = numpy.load('fusion_all_200_datatset.npy')
... positives = scores[0]
... negatives = scores[1]
# The negatives are mostly 0
>>> sum(negatives < 9e-16)
51911
>>> sum(negatives < 9e-17)
51525
>>> sum(negatives < 9e-18)
51029
>>> sum(negatives < 9e-20)
49675
>>> sum(negatives < 9e-22)
47543
>>> sum(negatives < 9e-30)
27487
>>> sum(negatives < 9e-60)
0

@tiago.pereira may be able to provide a set of smaller scores to debug this.

When I calculate the EER, I get 2.7%:

>>> scores  = numpy.load('fusion_all_200_datatset.npy')
... positives = scores[0]
... negatives = scores[1]
... 
>>> threshold = bob.measure.eer_threshold(negatives, positives)
... FAR, FRR = bob.measure.farfrr(negatives, positives, threshold)
... 
>>> FAR, FRR
(0.02762483029114497, 0.027626084163186636)
>>> negatives.mean(), negatives.std(), positives.mean(), positives.std()
(3.0290521114232657e-06, 0.00067550514259906739, 0.20795613959959214, 0.32512541269459205)
>>> 100*(FAR+FRR)/2
2.76254572271658
>>> bob.measure.plot.roc(negatives, positives, npoints)
[<matplotlib.lines.Line2D object at 0x7f472a8bed10>]
>>> bob.measure.plot.det(negatives, positives, npoints, color=(0,0,0), linestyle='-', label='test')
... bob.measure.plot.det_axis([0.1, 80, 0.1, 80])
... 
[-3.090232246772911, 0.8416212348748217, -3.090232246772911, 0.8416212348748217]
# plot the EER point on the DET curve
>>> pyplot.plot(bob.measure.ppndf(FAR), bob.measure.ppndf(FAR), 'ro')
[<matplotlib.lines.Line2D object at 0x7f472a4dc510>]

But when I plot the ROC and DET curves, I get curves with EER of 6% or more than 20%:

DET CURVE with around 6% EER:

ROC CURVE with more than 20% EER: