  In this article, we propose a general framework for multi-focal image
classification and authentication, the methodology being demonstrated on
microscope pollen images. The framework is meant to be generic and based on a
brute force-like approach aimed to be efficient not only on any kind, and any
number, of pollen images (regardless of the pollen type), but also on any kind
of multi-focal images. All stages of the framework's pipeline are designed to
be used in an automatic fashion. First, the optimal focus is selected using the
absolute gradient method. Then, pollen grains are extracted using a
coarse-to-fine approach involving both clustering and morphological techniques
(coarse stage), and a snake-based segmentation (fine stage). Finally, features
are extracted and selected using a generalized approach, and their
classification is tested with four classifiers: Weighted Neighbor Distance,
Neural Network, Decision Tree and Random Forest. The latter method, which has
shown the best and more robust classification accuracy results (above 97\% for
any number of pollen types), is finally used for the authentication stage.
