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ok, assuming, you get a std::vector<cv::Point2f> resultPnts; from dlib, you'd make a single 1d row Mat of those for both training and testing, and add a single label per landmark set:

Mat trainData; // initially empty
Mat trainLabels;
for each set of landmarks:
      std::vector<cv::Point2f> resultPnts = ...;
      Mat row(resultPoints, true); // deep copy
      trainData.push_back(row.reshape(1,1)); // one flat row per set
      trainLabel.push_back( theLabel );

so, for 200 sets, you get a [96*2 x 200] data Mat, and a [1 x 200] labels mat. prediction is similar:

      std::vector<cv::Point2f> resultPnts = ...;
      Mat row(resultPoints, true); // deep copy
      svm->predict(row.reshape(1,1));

while you cannot get a percentage confidence instead of the labels here, you can get the distance to the margin:

   float dist = svm->predict(query, noArray(), ml::StatModel::RAW_OUTPUT)

ok, assuming, you get a std::vector<cv::Point2f> resultPnts; from dlib, you'd make a single 1d row Mat of those for both training and testing, and add a single label per landmark set:

Mat trainData; // initially empty
Mat trainLabels;
for each set of landmarks:
      std::vector<cv::Point2f> resultPnts = ...;
      Mat row(resultPoints, true); // deep copy
row(resultPoints);
      trainData.push_back(row.reshape(1,1)); // one flat row per set
      trainLabel.push_back( theLabel );

so, for 200 sets, you get a [96*2 x 200] data Mat, and a [1 x 200] labels mat. prediction is similar:

      std::vector<cv::Point2f> resultPnts = ...;
      Mat row(resultPoints, true); // deep copy
row(resultPoints);
      svm->predict(row.reshape(1,1));

while you cannot get a percentage confidence instead of the labels here, you can get the distance to the margin:

   float dist = svm->predict(query, noArray(), ml::StatModel::RAW_OUTPUT)