Evaluating the Robustness of an Appearance-based Gaze Estimation Method for Multimodal Interfaces

Given the crucial role of eye movements on visual attention, tracking gaze behaviors is an important research problem in various applications including biometric identification, attention modeling and human-computer interaction. Most of the existing gaze tracking methods require a repetitive system calibration process and are sensitive to the user's head movements. Therefore, they cannot be easily implemented in current multimodal interfaces. This paper investigates an appearance-based approach for gaze estimation that requires minimum calibration and is robust against head motion. The approach consists in building an orthonormal basis, or eigenspace, of the eye appearance with principal component analysis (PCA). Unlike previous studies, we build the eigenspace using image patches displaying both eyes. The projections into the basis are used to train regression models which predict the gaze location. The approach is trained and tested with a new multimodal corpus introduced in this paper. We consider several variables such as the distance between user and the computer monitor, and head movement. The evaluation includes the performance of the proposed gaze estimation system with and without head movement. It also evaluates the results in subject-dependent versus subject-independent conditions under different distances. We report promising results which suggest that the proposed gaze estimation approach is a feasible and flexible scheme to facilitate gaze-based multimodal interfaces.