Estimation of eye gaze direction angles based on active appearance models
In this paper we demonstrate efficient methods for continuous estimation of eye gaze angles with application to sign language videos. The difficulty of the task lies on the fact that those videos contain images with low face resolution since they are recorded from distance. First, we proceed to the modeling of face and eyes region by training and fitting Global and Local Active Appearance Models (LAAM). Next, we propose a system for eye gaze estimation based on a machine learning approach. In the first stage of our method, we classify gaze into discrete classes using GMMs that are based either on the parameters of the LAAM, or on HOG descriptors for the eyes region. We also propose a method for computing gaze direction angles from GMM log-likelihoods. We qualitatively and quantitatively evaluate our methods on two sign language databases and compare with a state of the art geometric model of the eye based on LAAM landmarks, which provides an estimate in direction angles. Finally, we further evaluate our framework by getting ground truth data from an eye tracking system Our proposed methods, and especially the GMMs using LAAM parameters, demonstrate high accuracy and robustness even in challenging tasks.