Robust eye centers localization with zero-crossing encoded image projections

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 Labeled faces in the wild databases.

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