  This paper proposes a new framework for the eye centers localization by the
joint use of encoding of normalized image projections and a Multi Layer
Perceptron (MLP) classifier. The encoding is novel and it consists in
identifying the zero-crossings and extracting the relevant parameters from the
resulting modes. The compressed normalized projections produce feature
descriptors that are inputs to a properly-trained MLP, for discriminating among
various categories of image regions. The proposed framework forms a fast and
reliable system for the eye centers localization, especially in the context of
face expression analysis in unconstrained environments. We successfully test
the proposed method on a wide variety of databases including BioID,
Cohn-Kanade, Extended Yale B and Labelled Faces in the Wild (LFW) databases.
