User Independent Gaze Estimation by Exploiting Similarity Measures in the Eye Pair Appearance Eigenspace

Abstract

The design of gaze-based computer interfaces has been an active research area for over 40 years. One challenge of using gaze detectors is the repetitive calibration process required to adjust the parameters of the systems, and the constrained conditions imposed on the user for robust gaze estimation. We envision user-independent gaze detectors that do not require calibration, or any cooperation from the user. Toward this goal, we investigate an appearance-based approach, where we estimate the eigenspace for the gaze using principal component analysis (PCA). The projections are used as features of regression models that estimate the screen's coordinates. As expected, the performance of the approach decreases when the models are trained without data from the target user (i.e., user-independent condition). This study proposes an appealing training approach to bridge the gap in performance between user-dependent and user-independent conditions. Using the projections onto the eigenspace, the scheme identifies samples in training set that are similar to the testing images. We build the sample covariance matrix and the regression models only with these samples. We consider either similar frames or data from subjects with similar eye appearance. The promising results suggest that the proposed training approach is a feasible and convenient scheme for gaze-based multimodal interfaces.