Deep eye fixation map learning for calibration-free eye gaze tracking

Abstract

The existing eye trackers typically require an explicit personal calibration procedure to estimate subject-dependent eye parameters. Despite efforts in simplifying the calibration process, such a calibration process remains unnatural and bothersome, in particular for users of personal and mobile devices. To alleviate this problem, we introduce a technique that can eliminate explicit personal calibration. Based on combining a new calibration procedure with the eye fixation prediction, the proposed method performs implicit personal calibration without active participation or even knowledge of the user. Specifically, different from traditional deterministic calibration procedure that minimizes the differences between the predicted eye gazes and the actual eye gazes, we introduce a stochastic calibration procedure that minimizes the differences between the probability distribution of the predicted eye gaze and the distribution of the actual eye gaze. Furthermore, instead of using saliency map to approximate eye fixation distribution, we propose to use a regression based deep convolutional neural network (RCNN) that specifically learns image features to predict eye fixation. By combining the distribution based calibration with the deep fixation prediction procedure, personal eye parameters can be estimated without explicit user collaboration. We apply the proposed method to both 2D regression-based and 3D model-based eye gaze tracking methods. Experimental results show that the proposed method outperforms other implicit calibration methods and achieve comparable results to those that use traditional explicit calibration methods.