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not really an answer to your question, but you really should try the new, pretrained openface dnn:

dnn::Net net = dnn::readNetFromTorch("openface.nn4.small2.v1.t7");
Mat image = ... // color image !
Mat inputBlob = dnn::blobFromImage(image, 1./255, Size(96,96), Scalar(), true, false);
net.setInput(inputBlob);

Mat feature = net.forward().clone();

then just compare features from images, using either a straight L2 norm(like in the paper)

double dist = norm(feature1, feature2);

or any ml algorithm you like.