Appearance-based eye control system by manifold learning

Eye-movements are increasingly employed to study usability issues in HCI (Human-Computer Interacetion) contexts. In this paper we introduce our appearance-based eye control system which utilizes 5 specific eye movements, such as closed-eye movement and eye movements with gaze fixation at the positions (up, down, right, left) for HCI applications. In order to measure these eye movements, we employ a fast appeance-based gaze tracking method with manifold learning technique. First we propose to concatenate local eye appearance Center-Symmetric Local Binary Pattern(CS-LBP) descriptor for each subregion of eye image to form an eye appearance feature vector. The calibration phase is then introduced to construct a trainning samples by spectral clustering. After that, Laplacian Eigenmaps will be applied to the trainning set and unseen input together to get the structure of eye manifolds. Finally we can infer the eye movement of the new input by its distances with the clusters in the trainning set. Experimental results demonstrate that our system with quick 4-points calibration not only can reduce the run-time cost, but also provide another way to mesure eye movements without mesuring gaze coordinates to a HCI application such as our eye control system.