False Positives with Face recognition

Question

I have a CNN trained upon the images (cropped faces) of Mark Ruffalo. For my positive class I have around 200 images and for the negative datapoints I have sampled 200 random faces.

The model has a high recall but a very low precision. How could I increase the precision ?Also I am constrained by the number of positive images that I have. I am ready to compromise the recall in this tradeoff.

I have tried increasing the number of negative samples but that introduces a form of bias and the model starts classifying everything as negative to attain a local optima.

I have based my CNN upon overfeat:

local features = nn.Sequential()

features:add(nn.SpatialConvolutionMM(3, 96, 11, 11))
features:add(nn.ReLU())
features:add(nn.SpatialMaxPooling(2, 2, 2, 2))

features:add(nn.SpatialConvolutionMM(96, 256, 5, 5))
features:add(nn.ReLU())
features:add(nn.SpatialMaxPooling(2, 2, 2, 2))

features:add(nn.SpatialConvolutionMM(256, 512, 3, 3))
features:add(nn.ReLU())
features:add(nn.SpatialMaxPooling(2, 2, 2, 2))

-- 24x24x512
features:add(nn.SpatialConvolutionMM(512, 1024, 3, 3))
features:add(nn.ReLU())
features:add(nn.SpatialMaxPooling(2, 2, 2, 2))

--11x11x1024
features:add(nn.SpatialConvolutionMM(1024, 1024, 3, 3))
features:add(nn.ReLU())
features:add(nn.SpatialMaxPooling(2, 2, 2, 2))

-- 1.3. Create Classifier (fully connected layers)
local classifier = nn.Sequential()
classifier:add(nn.View(1024*4*4))
classifier:add(nn.Dropout(0.5))
classifier:add(nn.Linear(1024*4*4, 3072))
classifier:add(nn.Threshold(0, 1e-6))

classifier:add(nn.Dropout(0.5))
classifier:add(nn.Linear(3072, 4096))
classifier:add(nn.Threshold(0, 1e-6))

classifier:add(nn.Linear(4096, noutputs))




model = nn.Sequential():add(features):add(classifier)

Kindly Help

Answer 1

Try playing with the raw output of the CNN instead of taking the sign() of the output node (since it is a positive and negative class I assume there is only one output in the range [-1,1]).

For instance, for one sample, the output could be [0.9] indicating that the positive class should be picked. But if you play with this values, you can find a specific threshold value, hopefully, that gives you the precision you need. In other words, if you find that anything greater than [-0.35] should actually be chosen as the positive class because it gived you better precision, then -0.35 should be your threshold value.

This is where ROC analysis comes in handy.

Let me know if this helps.