A local and scale integrated feature descriptor in eye-gaze tracking

Eye-gaze tracking technology provides us with an unconventional way of Human Computer Interaction, and brings a lot of convenience with many practical applications and industrial products. The main focus of eye-gaze tracking is to calculate the model of gaze directions and the gaze coordinates. Based on computer vision, the key problem in the modeling process is the feature description. In this paper, a new descriptor, Local and Scale Integrated Feature (LoSIF) is proposed to extract eye-gaze movement features based on non-intrusive system, allowing slight head movements. The feature descriptor depends on two-level Haar wavelet transform, and carries on a combination of multi-resolution characteristics and effective dimension reduction algorithm, to achieve local and scale eye movement characterization. We use Support Vector Regression to estimate the mapping function between the appearance of the eyes and the corresponding gaze directions. As the experiment results show, the accuracy is within an acceptable range of 1 degree.