基于样本扩充和改进Lasso回归的视线估计

In order to make use of eye features for accurate line-of-sight estimation, a method based on sample expansion and improved Lasso regression was proposed to establish the mapping relationship between eye features and line of sight. Quality samples were obtained by scoring all samples, and then sample expansion was completed. The improved Lasso regression was used to obtain an accurate line-of-sight estimation model. This method is robust for interference such as blinking in the calibration process, and can still maintain a relatively high accuracy of line-of-sight estimation with interference. The experimental results show that the accuracy of sight estimation of this method is 11.25% higher than that of the traditional method without interference; the accuracy of sight estimation of this method is 22.62% higher than that of the traditional method with 6.67% abnormal data in the calibration data.