Gaze direction estimation from static images

Abstract

This study presents a novel multilevel algorithm for gaze direction recognition from static images. Proposed solution consists of three stages: (i) eye pupil localization using a multistage ellipse detector combined with a support vector machines verifier, (ii) eye bounding box localization calculated using a hybrid projection function and (iii) gaze direction classification using support vector machines and random forests. The proposed method has been tested on Eye-Chimera database with very promising results. Extensive tests show that eye bounding box localization allows us to achieve highly accurate results both in terms of eye location and gaze direction classification.