CODEWITHSUNDEEP
c++opencvimage-recognitionkeypointfeature-descriptor
BrainabilGH
BrainabilGH

Reputation: 474

What are the main rates and values we should figure to evaluate both feature detection, description and matching?

I work on palmprint recognition using feature2D with Open_CV library, and I use algorithms such as SIFT, SURF, ORB... to detect features and extract/match descriptors. My test include (1 vs 1) palmprint and also (1 vs Data Base) of palmprint.

Ones I get the result, I need to evaluate the algorithm, and for this I know that there are some rates or scores (like EER, rank-1 identification, recall and accuracy) which gives an estimation about how much this method was successful. Now I need to know if any of those rates are implemented in Open_CV, and how to use them. If they aren't, what are the different formulas used in the literary.

Upvotes: 0

Views: 51

Answers (1)

Julian
Julian

Reputation: 979

As far as I know there is little implemented in OpenCV. A common way is to store the results (e.g. in JSON) and process those with other programs such as Matlab or Python. This also allows you to change the evaluation without the need to recompute the algorithms.

There is no overall best method to show the results. It always depends on what you want to show. In my opinion ROC is the best way to express your output. It is also very widely used in research.

If you insist on doing it in C++, then you could use:

Roceasy or DLIB

Upvotes: 0

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